Methods and systems for detecting genetic variants

ABSTRACT

Disclosed herein in are methods and systems for determining genetic variants (e.g., copy number variation) in a polynucleotide sample. A method for determining copy number variations includes tagging double-stranded polynucleotides with duplex tags, sequencing polynucleotides from the sample and estimating total number of polynucleotides mapping to selected genetic loci. The estimate of total number of polynucleotides can involve estimating the number of double-stranded polynucleotides in the original sample for which no sequence reads are generated. This number can be generated using the number of polynucleotides for which reads for both complementary strands are detected and reads for which only one of the two complementary strands is detected.

CROSS-REFERENCE

This application is a continuation of U.S. application Ser. No.17/410,903, filed Aug. 24, 2021, which is a continuation of U.S.application Ser. No. 17/167,974, filed Feb. 4, 2021 (now U.S. Pat. No.11,149,307, issued Oct. 19, 2021), which is a continuation of U.S.application Ser. No. 16/945,124, filed Jul. 31, 2020 (now U.S. Pat. No.11,149,306, issued Oct. 19, 2021), which is a continuation of U.S.application Ser. No. 16/601,168, filed Oct. 14, 2019 (now U.S. Pat. No.10,801,063, issued Oct. 13, 2020), which is a continuation of U.S.application Ser. No. 15/892,178, filed Feb. 8, 2018 (now U.S. Pat. No.10,883,139, issued Jan. 5, 2021), which is a continuation of U.S.application Ser. No. 14/861,989, filed Sep. 22, 2015 (now U.S. Pat. No.9,920,366, issued Mar. 20, 2018), which is a continuation application ofInternational Application No. PCT/US2014/072383, filed Dec. 24, 2014,which application claims the benefit under 35 U.S.C. § 119(e) of U.S.Provisional Application No. 61/921,456, filed Dec. 28, 2013, and U.S.Provisional Application No. 61/948,509, filed Mar. 5, 2014, each ofwhich is entirely incorporated herein by reference.

BACKGROUND

The detection and quantification of polynucleotides is important formolecular biology and medical applications, such as diagnostics. Genetictesting is particularly useful for a number of diagnostic methods. Forexample, disorders that are caused by rare genetic alterations (e.g.,sequence variants) or changes in epigenetic markers, such as cancer andpartial or complete aneuploidy, may be detected or more accuratelycharacterized with DNA sequence information.

Early detection and monitoring of genetic diseases, such as cancer, isoften useful and needed in the successful treatment or management of thedisease. One approach may include the monitoring of a sample derivedfrom cell-free nucleic acids, a population of polynucleotides that canbe found in different types of bodily fluids. In some cases, disease maybe characterized or detected based on detection of genetic aberrations,such as copy number variation and/or sequence variation of one or morenucleic acid sequences, or the development of other certain rare geneticalterations. Cell-free DNA (cfDNA) may contain genetic aberrationsassociated with a particular disease. With improvements in sequencingand techniques to manipulate nucleic acids, there is a need in the artfor improved methods and systems for using cell-free DNA to detect andmonitor disease.

In particular, many methods have been developed for accurate copy numbervariation estimation, especially for heterogeneous genomic samples, suchas tumor-derived gDNA or for cfDNA for many applications (e.g.,prenatal, transplant, immune, metagenomics or cancer diagnostics). Mostof these methods include sample preparation whereby the original nucleicacids are converted into a sequenceable library, followed by massivelyparallel sequencing, and finally bioinformatics to estimate copy numbervariation at one or more loci.

SUMMARY

Although many of these methods are able to reduce or combat the errorsintroduced by the sample preparation and sequencing processes for allmolecules that are converted and sequenced, these methods are not ableto infer the counts of molecules that were converted but not sequenced.Since this count of converted by unsequenced molecules can be highlyvariable from genomic region to region, these counts can dramaticallyand adversely affect the sensitivity that can be achieved.

To address this issue, input double-stranded deoxyribonucleic acid (DNA)can be converted by a process that tags both halves of the individualdouble-stranded molecule, in some cases differently. This can beperformed using a variety of techniques, including ligation of hairpin,bubble, or forked adapters or other adaptors having double-stranded andsingle stranded segments (the unhybridized portion of a bubble, forkedor hairpin adapter are deemed single-stranded herein). If taggedcorrectly, each original Watson and Crick (i.e., strand) side of theinput double-stranded DNA molecule can be differently tagged andidentified by the sequencer and subsequent bioinformatics. For allmolecules in a particular region, counts of molecules where both Watsonand Crick sides were recovered (“Pairs”) versus those where only onehalf was recovered (“Singlets”) can be recorded. The number of unseenmolecules can be estimated based on the number of Pairs and Singletsdetected.

An aspect of the present disclosure provides a method for detectingand/or quantifying rare deoxyribonucleic acid (DNA) in a heterogeneouspopulation of original DNA fragments, comprising tagging the originalDNA fragments in a single reaction using a library of a plurality ofdifferent tags such that greater than 30% of the fragments are tagged atboth ends, wherein each of the tags comprises a molecular barcode. Thesingle reaction can be in a single reaction vessel. Greater than 50% ofthe fragments can be tagged at both ends. The plurality of differenttags can be no more than any of 100, 500, 1000, 10,000 or 100,000different tags.

Another aspect provides a set of library adaptors that can be used totag the molecules of interest (e.g., by ligation, hybridization, etc.).The set of library adaptors can comprise plurality of polynucleotidemolecules with molecular barcodes, wherein the plurality ofpolynucleotide molecules are less than or equal to 80 nucleotide basesin length, wherein the molecular barcodes are at least 4 nucleotidebases in length, and wherein (a) the molecular barcodes are differentfrom one another and have an edit distance of at least 1 between oneanother; (b) the molecular barcodes are located at least one nucleotidebase away from a terminal end of their respective polynucleotidemolecules; (c) optionally, at least one terminal base is identical inall of the polynucleotide molecules; and (d) none of the polynucleotidemolecules contains a complete sequencer motif.

In some embodiments, the library adaptors (or adapters) are identical toone another but for the molecular barcodes. In some embodiments, each ofthe plurality of library adaptors comprises at least one double-strandedportion and at least one single-stranded portion (e.g., anon-complementary portion or an overhang). In some embodiments, thedouble-stranded portion has a molecular barcode selected from acollection of different molecular barcodes. In some embodiments, thegiven molecular barcode is a randomer. In some embodiments, each of thelibrary adaptors further comprises a strand-identification barcode onthe at least one single-stranded portion. In some embodiments, thestrand-identification barcode includes at least 4 nucleotide bases. Insome embodiments, the single-stranded portion has a partial sequencermotif. In some embodiments, the library adaptors do not include acomplete sequencer motif.

In some embodiments, none of the library adaptors contains a sequencefor hybridizing to a flow cell or forming a hairpin for sequencing.

In some embodiments, all of the library adaptors have a terminal endwith nucleotide(s) that are the same. In some embodiments, the identicalterminal nucleotide(s) are over two or more nucleotide bases in length.

In some embodiments, each of the library adapters is Y-shaped, bubbleshaped or hairpin shaped. In some embodiments, none of the libraryadapters contains a sample identification motif. In some embodiments,each of the library adapters comprises a sequence that is selectivelyhybridizable to a universal primer. In some embodiments, each of thelibrary adapters comprises a molecular barcode that is at least 5, 6, 7,8, 9 and 10 nucleotide bases in length. In some embodiments, each of thelibrary adapters is from 10 nucleotide bases to 80 in length, or 30 to70 nucleotide bases in length, or 40 to 60 nucleotide bases in length.In some embodiments, at least 1, 2, 3, or 4 terminal bases are identicalin all of the library adaptors. In some embodiments, at least 4 terminalbases are identical in all of the library adaptors.

In some embodiments, the edit distance of the molecular barcodes of thelibrary adapters is a Hamming distance. In some embodiments, the editdistance is at least 1, 2, 3, 4 or 5. In some embodiments, the editdistance is with respect to individual bases of the plurality ofpolynucleotide molecules. In some embodiments, the molecular barcodesare located at least 10 nucleotide base away from a terminal end of anadapter. In some embodiments, the plurality of library adapters includesat least 2, 4, 6, 8, 10, 20, 30, 40 or 50 different molecular barcodes,or from 2-100, 4-80, 6-60 or 8-40 different molecular barcodes. In anyof the embodiments herein, there are more polynucleotides (e.g., cfDNAfragments) to be tagged than there are different molecular barcodes suchthat the tagging is not unique.

In some embodiments, the terminal end of an adaptor is configured forligation (e.g., to a target nucleic acid molecule). In some embodiments,the terminal end of an adaptor is a blunt end.

In some embodiments, the adaptors are purified and isolated. In someembodiments, the library comprises one or more non-naturally occurringbases.

In some embodiments, the polynucleotide molecules comprise a primersequence positioned 5′ with respect to the molecular barcodes.

In some embodiments, the set of library adaptors consists essentially ofthe plurality of polynucleotide molecules.

In another aspect, a method comprises (a) tagging a collection ofpolynucleotides with a plurality of polynucleotide molecules from alibrary of adaptors to create a collection of tagged polynucleotides;and (b) amplifying the collection of tagged polynucleotides in thepresence of sequencing adaptors, wherein the sequencing adaptors haveprimers with nucleotide sequences that are selectively hybridizable tocomplementary sequences in the plurality of polynucleotide molecules.The library of adaptors may be as described above or elsewhere herein.In some embodiments, each of the sequencer adaptors further comprises anindex tag, which can be a sample identification motif.

Another aspect, provides a method for detecting and/or quantifying rareDNA in a heterogeneous population of original DNA fragments, wherein therare DNA has a concentration that is less than 1%, the method comprising(a) tagging the original DNA fragments in a single reaction such thatgreater than 30% of the original DNA fragments are tagged at both endswith library adaptors that comprise molecular barcodes, therebyproviding tagged DNA fragments; (b) performing high-fidelityamplification on the tagged DNA fragments; (c) optionally, selectivelyenriching a subset of the tagged DNA fragments; (d) sequencing one orboth strands of the tagged, amplified and optionally selectivelyenriched DNA fragments to obtain sequence reads comprising nucleotidesequences of the molecular barcodes and at least a portion of theoriginal DNA fragments; (e) from the sequence reads, determiningconsensus reads that are representative of single-strands of theoriginal DNA fragments; and (f) quantifying the consensus reads todetect and/or quantify the rare DNA at a specificity that is greaterthan 99.9%.

In some embodiments, (e) comprises comparing sequence reads having thesame or similar molecular barcodes and the same or similar end offragment sequences. In some embodiments, the comparing further comprisesperforming a phylogentic analysis on the sequence reads having the sameor similar molecular barcodes. In some embodiments, the molecularbarcodes include a barcode having an edit distance of up to 3. In someembodiments, the end of fragment sequence includes fragment sequenceshaving an edit distance of up to 3.

In some embodiments, the method further comprises sorting sequence readsinto paired reads and unpaired reads, and quantifying a number of pairedreads and unpaired reads that map to each of one or more genetic loci.

In some embodiments, the tagging occurs by having an excess amount oflibrary adaptors as compared to original DNA fragments. In someembodiments, n the excess is at least a 5-fold excess. In someembodiments, the tagging comprises using a ligase. In some embodiments,the tagging comprises attachment to blunt ends.

In some embodiments, the method further comprises binning the sequencereads according to the molecular barcodes and sequence information fromat least one end of each of the original DNA fragments to create bins ofsingle stranded reads. In some embodiments, the method furthercomprises, in each bin, determining a sequence of a given original DNAfragment among the original DNA fragments by analyzing sequence reads.In some embodiments, the method further comprises detecting and/orquantifying the rare DNA by comparing a number of times each base occursat each position of a genome represented by the tagged, amplified, andoptionally enriched DNA fragments.

In some embodiments, the library adaptors do not contain completesequencer motifs. In some embodiments, the method further comprisesselectively enriching a subset of the tagged DNA fragments. In someembodiments, the method further comprises, after enriching, amplifyingthe enriched tagged DNA fragments in the presence of sequencing adaptorscomprising primers. In some embodiments, (a) provides tagged DNAfragments having from 2 to 1000 different combinations of molecularbarcodes.

In some embodiments, the DNA fragments are tagged with polynucleotidemolecules from a library of adaptors as described above or elsewhereherein.

In another aspect, a method for processing and/or analyzing a nucleicacid sample of a subject comprises (a) exposing polynucleotide fragmentsfrom the nucleic acid sample to a set of library adaptors to generatetagged polynucleotide fragments; and (b) subjecting the taggedpolynucleotide fragments to nucleic acid amplification reactions underconditions that yield amplified polynucleotide fragments asamplification products of the tagged polynucleotide fragments. The setof library adaptors comprises a plurality of polynucleotide moleculeswith molecular barcodes, wherein the plurality of polynucleotidemolecules are less than or equal to 80 nucleotide bases in length,wherein the molecular barcodes are at least 4 nucleotide bases inlength, and wherein (1) the molecular barcodes are different from oneanother and have an edit distance of at least 1 between one another; (2)the molecular barcodes are located at least one nucleotide base awayfrom a terminal end of their respective polynucleotide molecules; (3)optionally, at least one terminal base is identical in all of thepolynucleotide molecules; and (4) none of the polynucleotide moleculescontains a complete sequencer motif.

In some embodiments, the method further comprises determining nucleotidesequences of the amplified tagged polynucleotide fragments. In someembodiments, the nucleotide sequences of the amplified taggedpolynucleotide fragments are determined without polymerase chainreaction (PCR). In some embodiments, the method further comprisesanalyzing the nucleotide sequences with a programmed computer processorto identify one or more genetic variants in the nucleotide sample of thesubject. In some embodiments, the one or more genetic variants areselected from the group consisting of base change(s), insertion(s),repeat(s), deletion(s), copy number variation(s) and transversion(s). Insome embodiments, the one or more genetic variants include one or moretumor associated genetic alterations.

In some embodiments, the subject has or is suspected of having adisease. In some embodiments, the disease is cancer. In someembodiments, the method further comprises collecting the nucleic acidsample from the subject. In some embodiments, the nucleic acid sample iscollected from a location selected from the group consisting of blood,plasma, serum, urine, saliva, mucosal excretions, sputum, stool,cerebral spinal fluid and tears of the subject. In some embodiments, thenucleic acid sample is a cell-free nucleic acid sample. In someembodiments, the nucleic acid sample is collected from no more than 100nanograms (ng) of double-stranded polynucleotide molecules of thesubject.

In some embodiments, the polynucleotide fragments comprisedouble-stranded polynucleotide molecules. In some embodiments, in (a),the plurality of polynucleotide molecules couple to the polynucleotidefragments via blunt end ligation, sticky end ligation, molecularinversion probes, PCR, ligation-based PCR, multiplex PCR, singlestranded ligation, and single stranded circularization. In someembodiments, exposing the polynucleotide fragments of the nucleic acidsample to the plurality of polynucleotide molecules yields the taggedpolynucleotide fragments with a conversion efficiency of at least 10%.In some embodiments, any of at least 5%, 6%, 7%, 8%, 9%, 10%, 20%, or25% of the tagged polynucleotide fragments share a common polynucleotidemolecule or sequence. In some embodiments, the method further comprisesgenerating the polynucleotide fragments from the nucleic acid sample.

In some embodiments, the subjecting comprises amplifying the taggedpolynucleotide fragments from sequences corresponding to genes selectedfrom the group consisting of ALK, APC, BRAF, CDKN2A, EGFR, ERBB2, FBXW7,KRAS, MYC, NOTCH1, NRAS, PIK3CA, PTEN, RB1, TP53, MET, AR, ABL1, AKT1,ATM, CDH1, CSF1R, CTNNB1, ERBB4, EZH2, FGFR1, FGFR2, FGFR3, FLT3, GNA11,GNAQ, GNAS, HNF1A, HRAS, IDH1, IDH2, JAK2, JAK3, KDR, KIT, MLH1, MPL,NPM1, PDGFRA, PROC, PTPN11, RET, SMAD4, SMARCB1, SMO, SRC, STK11, VHL,TERT, CCND1, CDK4, CDKN2B, RAF1, BRCA1, CCND2, CDK6, NF1, TP53, ARID1A,BRCA2, CCNE1, ESR1, RIT1, GATA3, MAP2K1, RHEB, ROS1, ARAF, MAP2K2,NFE2L2, RHOA, and NTRK1.

In another aspect, a method comprises (a) generating a plurality ofsequence reads from a plurality of polynucleotide molecules, wherein theplurality of polynucleotide molecules cover genomic loci of a targetgenome, wherein the genomic loci correspond to a plurality of genesselected from the group consisting of ALK, APC, BRAF, CDKN2A, EGFR,ERBB2, FBXW7, KRAS, MYC, NOTCH1, NRAS, PIK3CA, PTEN, RB1, TP53, MET, AR,ABL1, AKT1, ATM, CDH1, CSF1R, CTNNB1, ERBB4, EZH2, FGFR1, FGFR2, FGFR3,FLT3, GNA11, GNAQ, GNAS, HNF1A, HRAS, IDH1, IDH2, JAK2, JAK3, KDR, KIT,MLH1, MPL, NPM1, PDGFRA, PROC, PTPN11, RET, SMAD4, SMARCB1, SMO, SRC,STK11, VHL, TERT, CCND1, CDK4, CDKN2B, RAF1, BRCA1, CCND2, CDK6, NF1,TP53, ARID1A, BRCA2, CCNE1, ESR1, RIT1, GATA3, MAP2K1, RHEB, ROS1, ARAF,MAP2K2, NFE2L2, RHOA, and NTRK1; (b) grouping with a computer processorthe plurality of sequence reads into families, wherein each familycomprises sequence reads from one of the template polynucleotides; (c)for each of the families, merging sequence reads to generate a consensussequence; (d) calling the consensus sequence at a given genomic locusamong the genomic loci; and (e) detecting at the given genomic locus anyof genetic variants among the calls, frequency of a genetic alterationamong the calls, total number of calls, and total number of alterationsamong the calls.

In some embodiments, each family comprises sequence reads from only oneof the template polynucleotides. In some embodiments, the given genomiclocus comprises at least one nucleic acid base. In some embodiments, thegiven genomic locus comprises a plurality of nucleic acid bases. In someembodiments, the calling comprises calling at least one nucleic acidbase at the given genomic locus. In some embodiments, the callingcomprises calling a plurality of nucleic acid bases at the given genomiclocus. In some embodiments, the calling comprises any one ofphylogenetic analysis, voting, weighing, assigning a probability to eachread at the locus in a family and calling the base with the highestprobability.

In some embodiments, the method further comprises performing (d)-(e) atan additional genomic locus among the genomic loci. In some embodiments,the method further comprises determining a variation in copy number atone of the given genomic locus and additional genomic locus based oncounts at the given genomic locus and additional genomic locus.

In some embodiments, the grouping comprises classifying the plurality ofsequence reads into families by identifying (i) different molecularbarcodes coupled to the plurality of polynucleotide molecules and (ii)similarities between the plurality of sequence reads, wherein eachfamily includes a plurality of nucleic acid sequences that areassociated with a different combination of molecular barcodes andsimilar or identical sequence reads. Different molecular barcodes havedifferent sequences.

In some embodiments, the consensus sequence is generated by evaluating aquantitative measure or a statistical significance level for each of thesequence reads. In some embodiments, the quantitative measure comprisesuse of a binomial distribution, exponential distribution, betadistribution, or empirical distribution. In some embodiments, the methodfurther comprises mapping the consensus sequence to the target genome.In some embodiments, the plurality of genes includes at least 2, 3, 4,5, 6, 7, 8, 9, 10, 20, 30, 40, 50 or all of the plurality of genesselected from the group.

Another aspect of the present disclosure provides a method, comprising(a) providing template polynucleotide molecules and a set of libraryadaptors in a single reaction vessel, wherein the library adaptors arepolynucleotide molecules that have different molecular barcodes (e.g.,from 2 to 1,000 different molecular barcodes), and wherein none of thelibrary adaptors contains a complete sequencer motif, (b) in the singlereaction vessel, coupling the library adaptors to the templatepolynucleotide molecules at an efficiency of at least 10%, therebytagging each template polynucleotide with a tagging combination that isamong a plurality of different tagging combinations (e.g., 4 to1,000,000 different tagging combinations), to produce taggedpolynucleotide molecules; (c) subjecting the tagged polynucleotidemolecules to an amplification reaction under conditions that yieldamplified polynucleotide molecules as amplification products of thetagged polynucleotide molecules; and (d) sequencing the amplifiedpolynucleotide molecules.

In some embodiments, the template polynucleotide molecules are bluntended or sticky-ended. In some embodiments, the library adaptors areidentical but for the molecular barcodes. In some embodiments, each ofthe library adaptors has a double stranded portion and at least onesingle-stranded portion. In some embodiments, the double-strandedportion has a molecular barcode among the molecular barcodes. In someembodiments, each of the library adaptors further comprises astrand-identification barcode on the at least one single-strandedportion. In some embodiments, the single-stranded portion has a partialsequencer motif. In some embodiments, the library adaptors have asequence of terminal nucleotides that are the same. In some embodiments,the template polynucleotide molecules are double-stranded. In someembodiments, the library adaptors couple to both ends of the templatepolynucleotide molecules.

In some embodiments, subjecting the tagged polynucleotide molecules tothe amplification reaction comprises non-specifically amplifying thetagged polynucleotide molecules.

In some embodiments, the amplification reaction comprises use of apriming site to amplify each of the tagged polynucleotide molecules. Insome embodiments, the priming site is a primer. In some embodiments, theprimer is a universal primer. In some embodiments, the priming site is anick.

In some embodiments, the method further comprises, prior to (e), (i)separating polynucleotide molecules comprising one or more givensequences from the amplified polynucleotide molecules, to produceenriched polynucleotide molecules; and (ii) amplifying the enrichedpolynucleotide molecules with sequencing adaptors.

In some embodiments, the efficiency is at least 30%, 40%, or 50%. Insome embodiments, the method further comprises identifying geneticvariants upon sequencing the amplified polynucleotide molecules. In someembodiments, the sequencing comprises (i) subjecting the amplifiedpolynucleotide molecules to an additional amplification reaction underconditions that yield additional amplified polynucleotide molecules asamplification products of the amplified polynucleotide molecules, and(ii) sequencing the additional amplified polynucleotide molecules. Insome embodiments, the additional amplification is performed in thepresence of sequencing adaptors.

In some embodiments, (b) and (c) are performed without aliquoting thetagged polynucleotide molecules. In some embodiments, the tagging isnon-unique tagging.

Another aspect, provides a system for analyzing a target nucleic acidmolecule of a subject, comprising a communication interface thatreceives nucleic acid sequence reads for a plurality of polynucleotidemolecules that cover genomic loci of a target genome; computer memorythat stores the nucleic acid sequence reads for the plurality ofpolynucleotide molecules received by the communication interface; and acomputer processor operatively coupled to the communication interfaceand the memory and programmed to (i) group the plurality of sequencereads into families, wherein each family comprises sequence reads fromone of the template polynucleotides, (ii) for each of the families,merge sequence reads to generate a consensus sequence, (iii) call theconsensus sequence at a given genomic locus among the genomic loci, and(iv) detect at the given genomic locus any of genetic variants among thecalls, frequency of a genetic alteration among the calls, total numberof calls; and total number of alterations among the calls, wherein thegenomic loci correspond to a plurality of genes selected from the groupconsisting of ALK, APC, BRAF, CDKN2A, EGFR, ERBB2, FBXW7, KRAS, MYC,NOTCH1, NRAS, PIK3CA, PTEN, RB1, TP53, MET, AR, ABL1, AKT1, ATM, CDH1,CSF1R, CTNNB1, ERBB4, EZH2, FGFR1, FGFR2, FGFR3, FLT3, GNA11, GNAQ,GNAS, HNF1A, HRAS, IDH1, IDH2, JAK2, JAK3, KDR, KIT, MLH1, MPL, NPM1,PDGFRA, PROC, PTPN11, RET, SMAD4, SMARCB1, SMO, SRC, STK11, VHL, TERT,CCND1, CDK4, CDKN2B, RAF1, BRCA1, CCND2, CDK6, NF1, TP53, ARID1A, BRCA2,CCNE1, ESR1, RIT1, GATA3, MAP2K1, RHEB, ROS1, ARAF, MAP2K2, NFE2L2,RHOA, and NTRK1.

In another aspect, a set of oligonucleotide molecules that selectivelyhybridize to at least 5 genes selected from the group consisting of ALK,APC, BRAF, CDKN2A, EGFR, ERBB2, FBXW7, KRAS, MYC, NOTCH1, NRAS, PIK3CA,PTEN, RB1, TP53, MET, AR, ABL1, AKT1, ATM, CDH1, CSF1R, CTNNB1, ERBB4,EZH2, FGFR1, FGFR2, FGFR3, FLT3, GNA11, GNAQ, GNAS, HNF1A, HRAS, IDH1,IDH2, JAK2, JAK3, KDR, KIT, MLH1, MPL, NPM1, PDGFRA, PROC, PTPN11, RET,SMAD4, SMARCB1, SMO, SRC, STK11, VHL, TERT, CCND1, CDK4, CDKN2B, RAF1,BRCA1, CCND2, CDK6, NF1, TP53, ARID1A, BRCA2, CCNE1, ESR1, RIT1, GATA3,MAP2K1, RHEB, ROS1, ARAF, MAP2K2, NFE2L2, RHOA, and NTRK1.

In some embodiments, the oligonucleotide molecules are from 10-200 basesin length. In some embodiments, the oligonucleotide moleculesselectively hybridize to exon regions of the at least 5 genes. In someembodiments, the oligonucleotide molecules selectively hybridize to atleast 30 exons in the at least 5 genes. In some embodiments, multipleoligonucleotide molecules selectively hybridize to each of the at least30 exons. In some embodiments, the oligonucleotide molecules thathybridize to each exon have sequences that overlap with at least 1 otheroligonucleotide molecule.

In another aspect, a kit comprises a first container containing aplurality of library adaptors each having a different molecular barcode;and a second container containing a plurality of sequencing adaptors,each sequencing adaptor comprising at least a portion of a sequencermotif and optionally a sample barcode. The library adaptors can be asdescribed above or elsewhere herein.

In some embodiments, the sequencing adaptor comprises the samplebarcode. In some embodiments, the library adaptors are blunt ended andY-shaped, and are less than or equal to 80 nucleic acid bases in length.In some embodiments, the sequencing adaptor is up to 70 bases from endto end.

In another aspect, a method for detecting sequence variants in a cellfree DNA sample, comprising detecting rare DNA at a concentration lessthan 1% with a specificity that is greater than 99.9%.

In another aspect, a method comprises detecting genetic variants in asample comprising DNA with a detection limit of at least 1% andspecificity greater than 99.9%. In some embodiments, the method furthercomprises converting cDNA (e.g. cfDNA) into adaptor tagged DNA with aconversion efficiency of at least 30%, 40%, or 50% and reducingsequencing noise (or distortion) by eliminating false positive sequencereads.

Another aspect provides a method, comprising (a) providing a samplecomprising a set of double-stranded polynucleotide molecules, eachdouble-stranded polynucleotide molecule including first and secondcomplementary strands; (b) tagging the double-stranded polynucleotidemolecules with a set of duplex tags, wherein each duplex tag differentlytags the first and second complementary strands of a double-strandedpolynucleotide molecule in the set; (c) sequencing at least some of thetagged strands to produce a set of sequence reads; (d) reducing and/ortracking redundancy in the set of sequence reads; (e) sorting sequencereads into paired reads and unpaired reads, wherein (i) each paired readcorresponds to sequence reads generated from a first tagged strand and asecond differently tagged complementary strand derived from adouble-stranded polynucleotide molecule in the set, and (ii) eachunpaired read represents a first tagged strand having no seconddifferently tag complementary strand derived from a double-strandedpolynucleotide molecule represented among the sequence reads in the setof sequence reads; (f) determining quantitative measures of (i) thepaired reads and (ii) the unpaired reads that map to each of one or moregenetic loci; and (g) estimating with a programmed computer processor aquantitative measure of total double-stranded polynucleotide moleculesin the set that map to each of the one or more genetic loci based on thequantitative measure of paired reads and unpaired reads mapping to eachlocus.

In some embodiments, the method further comprises (h) detecting copynumber variation in the sample by determining a normalized totalquantitative measure determined in step (g) at each of the one or moregenetic loci and determining copy number variation based on thenormalized measure. In some embodiments, the sample comprisesdouble-stranded polynucleotide molecules sourced substantially fromcell-free nucleic acids. In some embodiments, the duplex tags are notsequencing adaptors.

In some embodiments, reducing redundancy in the set of sequence readscomprises collapsing sequence reads produced from amplified products ofan original polynucleotide molecule in the sample back to the originalpolynucleotide molecule. In some embodiments, the method furthercomprises determining a consensus sequence for the originalpolynucleotide molecule. In some embodiments, the method furthercomprises identifying polynucleotide molecules at one or more geneticloci comprising a sequence variant. In some embodiments, the methodfurther comprises determining a quantitative measure of paired readsthat map to a locus, wherein both strands of the pair comprise asequence variant. In some embodiments, the method further comprisesdetermining a quantitative measure of paired molecules in which only onemember of the pair bears a sequence variant and/or determining aquantitative measure of unpaired molecules bearing a sequence variant.In some embodiments, the sequence variant is selected from the groupconsisting of a single nucleotide variant, an indel, a transversion, atranslocation, an inversion, a deletion, a chromosomal structurealteration, a gene fusion, a chromosome fusion, a gene truncation, agene amplification, a gene duplication and a chromosomal lesion.

Another aspect provides a system comprising a computer readable mediumcomprising machine-executable code that, upon execution by a computerprocessor, implements a method comprising (a) receiving into memory aset of sequence reads of polynucleotides tagged with duplex tags; (b)reducing and/or tracking redundancy in the set of sequence reads; (c)sorting sequence reads into paired reads and unpaired reads, wherein (i)each paired read corresponds to sequence reads generated from a firsttagged strand and a second differently tagged complementary strandderived from a double-stranded polynucleotide molecule in the set, and(ii) each unpaired read represents a first tagged strand having nosecond differently tag complementary strand derived from adouble-stranded polynucleotide molecule represented among the sequencereads in the set of sequence reads; (d) determining quantitativemeasures of (i) the paired reads and (ii) the unpaired reads that map toeach of one or more genetic loci; and (e) estimating a quantitativemeasure of total double-stranded polynucleotide molecules in the setthat map to each of the one or more genetic loci based on thequantitative measure of paired reads and unpaired reads mapping to eachlocus.

Another aspect provides a method, comprising (a) providing a samplecomprising a set of double-stranded polynucleotide molecules, eachdouble-stranded polynucleotide molecule including first and secondcomplementary strands; (b) tagging the double-stranded polynucleotidemolecules with a set of duplex tags, wherein each duplex tag differentlytags the first and second complementary strands of a double-strandedpolynucleotide molecule in the set; (c) sequencing at least some of thetagged strands to produce a set of sequence reads; (d) reducing and/ortracking redundancy in the set of sequence reads; (e) sorting sequencereads into paired reads and unpaired reads, wherein (i) each paired readcorresponds to sequence reads generated from a first tagged strand and asecond differently tagged complementary strand derived from adouble-stranded polynucleotide molecule in the set, and (ii) eachunpaired read represents a first tagged strand having no seconddifferently tag complementary strand derived from a double-strandedpolynucleotide molecule represented among the sequence reads in the setof sequence reads; and (f) determining quantitative measures of at leasttwo of (i) the paired reads, (ii) the unpaired reads that map to each ofone or more genetic loci, (iii) read depth of the paired reads and (iv)read depth of unpaired reads.

In some embodiments, (f) comprises determining quantitative measures ofat least three of (i)-(iv). In some embodiments, (f) comprisesdetermining quantitative measures of all of (i)-(iv). In someembodiments, the method further comprises (g) estimating with aprogrammed computer processor a quantitative measure of totaldouble-stranded polynucleotide molecules in the set that map to each ofthe one or more genetic loci based on the quantitative measure of pairedreads and unpaired reads and their read depths mapping to each locus.

In another aspect, a method comprises (a) tagging control parentpolynucleotides with a first tag set to produce tagged control parentpolynucleotides, wherein the first tag set comprises a plurality oftags, wherein each tag in the first tag set comprises a same control tagand an identifying tag, and wherein the tag set comprises a plurality ofdifferent identifying tags; (b) tagging test parent polynucleotides witha second tag set to produce tagged test parent polynucleotides, whereinthe second tag set comprises a plurality of tags, wherein each tag inthe second tag set comprises a same test tag that is distinguishablefrom the control tag and an identifying tag, and wherein the second tagset comprises a plurality of different identifying tags; (c) mixingtagged control parent polynucleotides with tagged test parentpolynucleotides to form a pool; (d) amplifying tagged parentpolynucleotides in the pool to form a pool of amplified, taggedpolynucleotides; (e) sequencing amplified, tagged polynucleotides in theamplified pool to produce a plurality of sequence reads; (f) groupingsequence reads into families, each family comprising sequence readsgenerated from a same parent polynucleotide, which grouping isoptionally based on information from an identifying tag and fromstart/end sequences of the parent polynucleotides, and, optionally,determining a consensus sequence for each of a plurality of parentpolynucleotides from the plurality of sequence reads in a group; (g)classifying each family or consensus sequence as a control parentpolynucleotide or as a test parent polynucleotide based on having a testtag or a control tag; (h) determining a quantitative measure of controlparent polynucleotides and control test polynucleotides mapping to eachof at least two genetic loci; and (i) determining copy number variationin the test parent polynucleotides at at least one locus based onrelative quantity of test parent polynucleotides and control parentpolynucleotides mapping to the at least one locus.

In another aspect, a method comprises (a) generating a plurality ofsequence reads from a plurality of template polynucleotides, eachpolynucleotide mapped to a genomic locus; (b) grouping the sequencereads into families, each family comprising sequence reads generatedfrom one of the template polynucleotides; (c) calling a base (orsequence) at the genomic locus for each of the families; (d) detectingat the genomic locus any of genomic alterations among the calls,frequency of a genetic alteration among the calls, total number of callsand total number of alterations among the calls.

In some embodiments, calling comprises any of phylogenetic analysis,voting, weighing, assigning a probability to each read at the locus in afamily, and calling the base with the highest probability. In someembodiments, the method is performed at two loci, comprising determiningCNV at one of the loci based on counts at each of the loci.

Another aspect provides a method for determining a quantitative measureindicative of a number of individual double-stranded DNA fragments in asample comprising (a) determining a quantitative measure of individualDNA molecules for which both strands are detected; (b) determining aquantitative measure of individual DNA molecules for which only one ofthe DNA strands are detected; (c) inferring from (a) and (b) above aquantitative measure of individual DNA molecules for which neitherstrand was detected; and (d) using (a)-(c) determining the quantitativemeasure indicative of a number of individual double-stranded DNAfragments in the sample.

In some embodiments, the method further comprises detecting copy numbervariation in the sample by determining a normalized quantitative measuredetermined in step (d) at each of one or more genetic loci anddetermining copy number variation based on the normalized measure. Insome embodiments, the sample comprises double-stranded polynucleotidemolecules sourced substantially from cell-free nucleic acids.

In some embodiments, determining the quantitative measure of individualDNA molecules comprises tagging the DNA molecules with a set of duplextags, wherein each duplex tag differently tags complementary strands ofa double-stranded DNA molecule in the sample to provide tagged strands.In some embodiments, the method further comprises sequencing at leastsome of the tagged strands to produce a set of sequence reads. In someembodiments, the method further comprises sorting sequence reads intopaired reads and unpaired reads, wherein (i) each paired readcorresponds to sequence reads generated from a first tagged strand and asecond differently tagged complementary strand derived from adouble-stranded polynucleotide molecule in the set, and (ii) eachunpaired read represents a first tagged strand having no seconddifferently tag complementary strand derived from a double-strandedpolynucleotide molecule represented among the sequence reads in the setof sequence reads. In some embodiments, the method further comprisesdetermining quantitative measures of (i) the paired reads and (ii) theunpaired reads that map to each of one or more genetic loci to determinea quantitative measure of total double-stranded DNA molecules in thesample that map to each of the one or more genetic loci based on thequantitative measure of paired reads and unpaired reads mapping to eachlocus.

In another aspect, a method for reducing distortion in a sequencingassay, comprises (a) tagging control parent polynucleotides with a firsttag set to produce tagged control parent polynucleotides; (b) taggingtest parent polynucleotides with a second tag set to produce tagged testparent polynucleotides; (c) mixing tagged control parent polynucleotideswith tagged test parent polynucleotides to form a pool; (d) determiningquantities of tagged control parent polynucleotides and tagged testparent polynucleotides; and (e) using the quantities of tagged controlparent polynucleotides to reduce distortion in the quantities of taggedtest parent polynucleotides.

In some embodiments, the first tag set comprises a plurality of tags,wherein each tag in the first tag set comprises a same control tag andan identifying tag, and wherein the first tag set comprises a pluralityof different identifying tags. In some embodiments, the second tag setcomprises a plurality of tags, wherein each tag in the second tag setcomprises a same test tag and an identifying tag, wherein the test tagis distinguishable from the control tag, and wherein the second tag setcomprises a plurality of different identifying tags. In someembodiments, (d) comprises amplifying tagged parent polynucleotides inthe pool to form a pool of amplified, tagged polynucleotides, andsequencing amplified, tagged polynucleotides in the amplified pool toproduce a plurality of sequence reads. In some embodiments, the methodfurther comprises grouping sequence reads into families, each familycomprising sequence reads generated from a same parent polynucleotide,which grouping is optionally based on information from an identifyingtag and from start/end sequences of the parent polynucleotides, and,optionally, determining a consensus sequence for each of a plurality ofparent polynucleotides from the plurality of sequence reads in a group.

In some embodiments, (d) comprises determining copy number variation inthe test parent polynucleotides at greater than or equal to one locusbased on relative quantity of test parent polynucleotides and controlparent polynucleotides mapping to the locus.

Another aspect provides a method comprising (a) ligating adaptors todouble-stranded DNA polynucleotides, wherein ligating is performed in asingle reaction vessel, and wherein the adaptors comprise molecularbarcodes, to produce a tagged library comprising an insert from thedouble-stranded DNA polynucleotides, and having between 4 and 1 milliondifferent tags; (b) generating a plurality of sequence reads for each ofthe double-stranded DNA polynucleotides in the tagged library; (c)grouping sequence reads into families, each family comprising sequencereads generated from a single DNA polynucleotide among thedouble-stranded DNA polynucleotides, based on information in a tag andinformation at an end of the insert; and (d) calling bases at eachposition in the double-stranded DNA molecule based on bases at theposition in members of a family. In some embodiments, (b) comprisesamplifying each of the double-stranded DNA polynucleotide molecules inthe tagged library to generate amplification products, and sequencingthe amplification products. In some embodiments, the method furthercomprises sequencing the double-stranded DNA polynucleotide molecules aplurality of times. In some embodiments, (b) comprises sequencing theentire insert. In some embodiments, (c) further comprises collapsingsequence reads in each family to generate a consensus sequence. In someembodiments, (d) comprises calling a plurality of sequential bases fromat least a subset of the sequence reads to identify single nucleotidevariations (SNV) in the double-stranded DNA molecule.

Another aspect provides a method of detecting disease cell heterogeneityfrom a sample comprising polynucleotides from somatic cells and diseasecells. The method comprises quantifying polynucleotides in the samplebearing a nucleotide sequence variant at each of a plurality of geneticloci; determining copy number variation (CNV) at each of the pluralityof genetic loci, wherein the CNV indicates a genetic dose of a locus inthe disease cell polynucleotides; determining with a programmed computerprocessor a relative measure of quantity of polynucleotides bearing asequence variant at a locus per the genetic dose at the locus for eachof a plurality of the loci; and comparing the relative measures at eachof the plurality of loci, wherein different relative measures isindicative of tumor heterogeneity.

In another aspect, a method comprises subjecting a subject to one ormore pulsed therapy cycles, each pulsed therapy cycle comprising (a) afirst period during which a drug is administered at a first amount; and(b) a second period during which the drug is administered at a second,reduced amount, wherein (i) the first period is characterized by a tumorburden detected above a first clinical level; and (ii) the second periodis characterized by a tumor burden detected below a second clinicallevel.

Additional aspects and advantages of the present disclosure will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only illustrative embodiments of thepresent disclosure are shown and described. As will be realized, thepresent disclosure is capable of other and different embodiments, andits several details are capable of modifications in various obviousrespects, all without departing from the disclosure. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings (also “figure” and “FIG.” herein), of which:

FIG. 1 is a flowchart representation of a method of the presentdisclosure for determining copy number variation (CNV);

FIG. 2 depicts mapping of pairs and singlets to Locus A and Locus B in agenome;

FIG. 3 shows a reference sequence encoding a genetic Locus A;

FIGS. 4A-C shows amplification, sequencing, redundancy reduction andpairing of complementary molecules;

FIG. 5 shows increased confidence in detecting sequence variants bypairing reads from Watson and Crick strands;

FIG. 6 shows a computer system that is programmed or otherwiseconfigured to implement various methods of the present disclosure;

FIG. 7 is schematic representation of a system for analyzing a samplecomprising nucleic acids from a user, including a sequencer;bioinformatic software and internet connection for report analysis by,for example, a hand held device or a desk top computer;

FIG. 8 is a flowchart representation of a method of this invention fordetermining CNV using pooled test and control pools; and

FIGS. 9A-9C schematically illustrate a method for tagging apolynucleotide molecule with a library adaptor and subsequently asequencing adaptor.

DETAILED DESCRIPTION

While various embodiments of the invention have been shown and describedherein, it will be obvious to those skilled in the art that suchembodiments are provided by way of example only. Numerous variations,changes, and substitutions may occur to those skilled in the art withoutdeparting from the invention. It should be understood that variousalternatives to the embodiments of the invention described herein may beemployed.

The term “genetic variant,” as used herein, generally refers to analteration, variant or polymorphism in a nucleic acid sample or genomeof a subject. Such alteration, variant or polymorphism can be withrespect to a reference genome, which may be a reference genome of thesubject or other individual. Single nucleotide polymorphisms (SNPs) area form of polymorphisms. In some examples, one or more polymorphismscomprise one or more single nucleotide variations (SNVs), insertions,deletions, repeats, small insertions, small deletions, small repeats,structural variant junctions, variable length tandem repeats, and/orflanking sequences. Copy number variants (CNVs), transversions and otherrearrangements are also forms of genetic variation. A genomicalternation may be a base change, insertion, deletion, repeat, copynumber variation, or transversion.

The term “polynucleotide,” as used herein, generally refers to amolecule comprising one or more nucleic acid subunits. A polynucleotidecan include one or more subunits selected from adenosine (A), cytosine(C), guanine (G), thymine (T) and uracil (U), or variants thereof. Anucleotide can include A, C, G, T or U, or variants thereof. Anucleotide can include any subunit that can be incorporated into agrowing nucleic acid strand. Such subunit can be an A, C, G, T, or U, orany other subunit that is specific to one or more complementary A, C, G,T or U, or complementary to a purine (i.e., A or G, or variant thereof)or a pyrimidine (i.e., C, T or U, or variant thereof). A subunit canenable individual nucleic acid bases or groups of bases (e.g., AA, TA,AT, GC, CG, CT, TC, GT, TG, AC, CA, or uracil-counterparts thereof) tobe resolved. In some examples, a polynucleotide is deoxyribonucleic acid(DNA) or ribonucleic acid (RNA), or derivatives thereof. Apolynucleotide can be single-stranded or double stranded.

The term “subject,” as used herein, generally refers to an animal, suchas a mammalian species (e.g., human) or avian (e.g., bird) species, orother organism, such as a plant. More specifically, the subject can be avertebrate, a mammal, a mouse, a primate, a simian or a human. Animalsinclude, but are not limited to, farm animals, sport animals, and pets.A subject can be a healthy individual, an individual that has or issuspected of having a disease or a pre-disposition to the disease, or anindividual that is in need of therapy or suspected of needing therapy. Asubject can be a patient.

The term “genome” generally refers to an entirety of an organism'shereditary information. A genome can be encoded either in DNA or in RNA.A genome can comprise coding regions that code for proteins as well asnon-coding regions. A genome can include the sequence of all chromosomestogether in an organism. For example, the human genome has a total of 46chromosomes. The sequence of all of these together constitutes a humangenome.

The terms “adaptor(s)”, “adapter(s)” and “tag(s)” are used synonymouslythroughout this specification. An adaptor or tag can be coupled to apolynucleotide sequence to be “tagged” by any approach includingligation, hybridization, or other approaches.

The term “library adaptor” or “library adapter” as used herein,generally refers to a molecule (e.g., polynucleotide) whose identity(e.g., sequence) can be used to differentiate polynucleotides in abiological sample (also “sample” herein).

The term “sequencing adaptor,” as used herein, generally refers to amolecule (e.g., polynucleotide) that is adapted to permit a sequencinginstrument to sequence a target polynucleotide, such as by interactingwith the target polynucleotide to enable sequencing. The sequencingadaptor permits the target polynucleotide to be sequenced by thesequencing instrument. In an example, the sequencing adaptor comprises anucleotide sequence that hybridizes or binds to a capture polynucleotideattached to a solid support of a sequencing system, such as a flow cell.In another example, the sequencing adaptor comprises a nucleotidesequence that hybridizes or binds to a polynucleotide to generate ahairpin loop, which permits the target polynucleotide to be sequenced bya sequencing system. The sequencing adaptor can include a sequencermotif, which can be a nucleotide sequence that is complementary to aflow cell sequence of other molecule (e.g., polynucleotide) and usableby the sequencing system to sequence the target polynucleotide. Thesequencer motif can also include a primer sequence for use insequencing, such as sequencing by synthesis. The sequencer motif caninclude the sequence(s) needed to couple a library adaptor to asequencing system and sequence the target polynucleotide.

As used herein the terms “at least”, “at most” or “about”, whenpreceding a series, refers to each member of the series, unlessotherwise identified.

The term “about” and its grammatical equivalents in relation to areference numerical value can include a range of values up to plus orminus 10% from that value. For example, the amount “about 10” caninclude amounts from 9 to 11. In other embodiments, the term “about” inrelation to a reference numerical value can include a range of valuesplus or minus 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1% from thatvalue.

The term “at least” and its grammatical equivalents in relation to areference numerical value can include the reference numerical value andgreater than that value. For example, the amount “at least 10” caninclude the value 10 and any numerical value above 10, such as 11, 100,and 1,000.

The term “at most” and its grammatical equivalents in relation to areference numerical value can include the reference numerical value andless than that value. For example, the amount “at most 10” can includethe value 10 and any numerical value under 10, such as 9, 8, 5, 1, 0.5,and 0.1.

1. Methods for Processing and/or Analyzing a Nucleic Acid Sample

An aspect of the present disclosure provides methods for determining agenomic alternation in a nucleic acid sample of a subject. FIG. 1 showsa method of determining copy number variation (CNV). The method can beimplemented to determine other genomic alternations, such as SNVs.

A. Polynucleotide Isolation

Methods disclosed herein can comprise isolating one or morepolynucleotides. A polynucleotide can comprise any type of nucleic acid,for example, a sequence of genomic nucleic acid, or an artificialsequence (e.g., a sequence not found in genomic nucleic acid). Forexample, an artificial sequence can contain non-natural nucleotides.Also, a polynucleotide can comprise both genomic nucleic acid and anartificial sequence, in any portion. For example, a polynucleotide cancomprise 1 to 99% of genomic nucleic acid and 99% to 1% of artificialsequence, where the total adds up to 100%. Thus, fractions ofpercentages are also contemplated. For example, a ratio of 99.1% to 0.9%is contemplated.

A polynucleotide can comprise any type of nucleic acids, such as DNAand/or RNA. For example, if a polynucleotide is DNA, it can be genomicDNA, complementary DNA (cDNA), or any other deoxyribonucleic acid. Apolynucleotide can also be cell-free DNA (cfDNA). For example, thepolynucleotide can be circulating DNA. The circulating DNA can comprisecirculating tumor DNA (ctDNA). A polynucleotide can be double-strandedor single-stranded. Alternatively, a polynucleotide can comprise acombination of a double-stranded portion and a single-stranded portion.

Polynucleotides do not have to be cell-free. In some cases, thepolynucleotides can be isolated from a sample. For example, in step(102) (FIG. 1), double-stranded polynucleotides are isolated from asample. A sample can be any biological sample isolated from a subject.For example, a sample can comprise, without limitation, bodily fluid,whole blood, platelets, serum, plasma, stool, red blood cells, whiteblood cells or leucocytes, endothelial cells, tissue biopsies, synovialfluid, lymphatic fluid, ascites fluid, interstitial or extracellularfluid, the fluid in spaces between cells, including gingival crevicularfluid, bone marrow, cerebrospinal fluid, saliva, mucous, sputum, semen,sweat, urine, or any other bodily fluids. A bodily fluid can includesaliva, blood, or serum. For example, a polynucleotide can be cell-freeDNA isolated from a bodily fluid, e.g., blood or serum. A sample canalso be a tumor sample, which can be obtained from a subject by variousapproaches, including, but not limited to, venipuncture, excretion,ejaculation, massage, biopsy, needle aspirate, lavage, scraping,surgical incision, or intervention or other approaches.

A sample can comprise various amount of nucleic acid that containsgenome equivalents. For example, a sample of about 30 ng DNA can containabout 10,000 (10⁴) haploid human genome equivalents and, in the case ofcfDNA, about 200 billion (2×10¹¹) individual polynucleotide molecules.Similarly, a sample of about 100 ng of DNA can contain about 30,000haploid human genome equivalents and, in the case of cfDNA, about 600billion individual molecules.

A sample can comprise nucleic acids from different sources. For example,a sample can comprise germline DNA or somatic DNA. A sample can comprisenucleic acids carrying mutations. For example, a sample can comprise DNAcarrying germline mutations and/orsomatic mutations. A sample can alsocomprise DNA carrying cancer-associated mutations (e.g.,cancer-associated somatic mutations).

B. Tagging

Polynucleotides disclosed herein can be tagged. For example, in step(104) (FIG. 1) the double-stranded polynucleotides are tagged withduplex tags, tags that differently label the complementary strands(i.e., the “Watson” and “Crick” strands) of a double-stranded molecule.In one embodiment the duplex tags are polynucleotides havingcomplementary and non-complementary portions.

Tags can be any types of molecules attached to a polynucleotide,including, but not limited to, nucleic acids, chemical compounds,florescent probes, or radioactive probes. Tags can also beoligonucleotides (e.g., DNA or RNA). Tags can comprise known sequences,unknown sequences, or both. A tag can comprise random sequences,pre-determined sequences, or both. A tag can be double-stranded orsingle-stranded. A double-stranded tag can be a duplex tag. Adouble-stranded tag can comprise two complementary strands.Alternatively, a double-stranded tag can comprise a hybridized portionand a non-hybridized portion. The double-stranded tag can be Y-shaped,e.g., the hybridized portion is at one end of the tag and thenon-hybridized portion is at the opposite end of the tag. One suchexample are the “Y adapters” used in Illumina sequencing. Other examplesinclude hairpin shaped adapters or bubble shaped adapters. Bubble shapedadapters have non-complementary sequences flanked on both sides bycomplementary sequences.

Tagging disclosed herein can be performed using any method. Apolynucleotide can be tagged with an adaptor by hybridization. Forexample, the adaptor can have a nucleotide sequence that iscomplementary to at least a portion of a sequence of the polynucleotide.As an alternative, a polynucleotide can be tagged with an adaptor byligation.

For example, tagging can comprise using one or more enzymes. The enzymecan be a ligase. The ligase can be a DNA ligase. For example, the DNAligase can be a T4 DNA ligase, E. coli DNA ligase, and/or mammalianligase. The mammalian ligase can be DNA ligase I, DNA ligase III, or DNAligase IV. The ligase can also be a thermostable ligase. Tags can beligated to a blunt-end of a polynucleotide (blunt-end ligation).Alternatively, tags can be ligated to a sticky end of a polynucleotide(sticky-end ligation). Efficiency of ligation can be increased byoptimizing various conditions. Efficiency of ligation can be increasedby optimizing the reaction time of ligation. For example, the reactiontime of ligation can be less than 12 hours, e.g., less than 1, less than2, less than 3, less than 4, less than 5, less than 6, less than 7, lessthan 8, less than 9, less than 10, less than 11, less than 12, less than13, less than 14, less than 15, less than 16, less than 17, less than18, less than 19, or less than 20 hours. In a particular example,reaction time of ligation is less than 20 hours. Efficiency of ligationcan be increased by optimizing the ligase concentration in the reaction.For example, the ligase concentration can be at least 10, at least 50,at least 100, at least 150, at least 200, at least 250, at least 300, atleast 400, at least 500, or at least 600 unit/microliter. Efficiency canalso be optimized by adding or varying the concentration of an enzymesuitable for ligation, enzyme cofactors or other additives, and/oroptimizing a temperature of a solution having the enzyme. Efficiency canalso be optimized by varying the addition order of various components ofthe reaction. The end of tag sequence can comprise dinucleotide toincrease ligation efficiency. When the tag comprises a non-complementaryportion (e.g., Y-shaped adaptor), the sequence on the complementaryportion of the tag adaptor can comprise one or more selected sequencesthat promote ligation efficiency. Preferably such sequences are locatedat the terminal end of the tag. Such sequences can comprise 1, 2, 3, 4,5, or 6 terminal bases. Reaction solution with high viscosity (e.g., alow Reynolds number) can also be used to increase ligation efficiency.For example, solution can have a Reynolds number less than 3000, lessthan 2000, less than 1000, less than 900, less than 800, less than 700,less than 600, less than 500, less than 400, less than 300, less than200, less than 100, less than 50, less than 25, or less than 10. It isalso contemplated that roughly unified distribution of fragments (e.g.,tight standard deviation) can be used to increase ligation efficiency.For example, the variation in fragment sizes can vary by less than 20%,less than 15%, less than 10%, less than 5%, or less than 1%. Tagging canalso comprise primer extension, for example, by polymerase chainreaction (PCR). Tagging can also comprise any of ligation-based PCR,multiplex PCR, single strand ligation, or single strand circularization.

In some instances, the tags herein comprise molecular barcodes. Suchmolecular barcodes can be used to differentiate polynucleotides in asample. Preferably molecular barcodes are different from one another.For example, molecular barcodes can have a difference between them thatcan be characterized by a predetermined edit distance or a Hammingdistance. In some instances, the molecular barcodes herein have aminimum edit distance of 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10. To furtherimprove efficiency of conversion (e.g., tagging) of untagged molecularto tagged molecules, one preferably utilizes short tags. For example, insome embodiments, a library adapter tag can be up to 65, 60, 55, 50, 45,40, or 35 nucleotide bases in length. A collection of such short librarybarcodes preferably includes a number of different molecular barcodes,e.g., at least 2, 4, 6, 8, 10, 12, 14, 16, 18 or 20 different barcodeswith a minimum edit distance of 1, 2, 3 or more.

Thus, a collection of molecules can include one or more tags. In someinstances, some molecules in a collection can include an identifying tag(“identifier”) such as a molecular barcode that is not shared by anyother molecule in the collection. For example, in some instances of acollection of molecules, at least 50%, at least 51%, at least 52%, atleast 53%, at least 54%, at least 55%, at least 56%, at least 57%, atleast 58%, at least 59%, at least 60%, at least 61%, at least 62%, atleast 63%, at least 64%, at least 65%, at least 66%, at least 67%, atleast 68%, at least 69%, at least 70%, at least 71%, at least 72%, atleast 73%, at least 74%, at least 75%, at least 76%, at least 77%, atleast 78%, at least 79%, at least 80%, at least 81%, at least 82%, atleast 83%, at least 84%, at least 85%, at least 86%, at least 87%, atleast 88%, at least 89%, at least 90%, at least 91%, at least 92%, atleast 93%, at least 94%, at least 95%, at least 96%, at least 97%, atleast 98%, at least 99%, or 100% of the molecules in the collection caninclude an identifier or molecular barcode that is not shared by anyother molecule in the collection. As used herein, a collection ofmolecules is considered to be “uniquely tagged” if each of at least 95%of the molecules in the collection bears an identifier that is notshared by any other molecule in the collection (“unique tag” or “uniqueidentifier”). A collection of molecules is considered to be“non-uniquely tagged” if each of at least 1%, at least 5%, at least 10%,at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, atleast 40%, at least 45%, or at least or about 50% of the molecules inthe collection bears an identifying tag or molecular barcode that isshared by at least one other molecule in the collection (“non-uniquetag” or “non-unique identifier”). Accordingly, in a non-uniquely taggedpopulation no more than 1% of the molecules are uniquely tagged. Forexample, in a non-uniquely tagged population, no more than 1%, 5%, 10%,15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50% of the molecules can beuniquely tagged.

A number of different tags can be used based on the estimated number ofmolecules in a sample. In some tagging methods, the number of differenttags can be at least the same as the estimated number of molecules inthe sample. In other tagging methods, the number of different tags canbe at least two, three, four, five, six, seven, eight, nine, ten, onehundred or one thousand times as many as the estimated number ofmolecules in the sample. In unique tagging, at least two times (or more)as many different tags can be used as the estimated number of moleculesin the sample.

The molecules in the sample may be non-uniquely tagged. In suchinstances a fewer number of tags or molecular barcodes is used then thenumber of molecules in the sample to be tagged. For example, no morethan 100, 50, 40, 30, 20 or 10 unique tags or molecular barcodes areused to tag a complex sample such as a cell free DNA sample with manymore different fragments.

The polynucleotide to be tagged can be fragmented, such as eithernaturally or using other approaches, such as, for example, shearing. Thepolynucleotides can be fragmented by certain methods, including but notlimited to, mechanical shearing, passing the sample through a syringe,sonication, heat treatment (e.g., for 30 minutes at 90° C.), and/ornuclease treatment (e.g., using DNase, RNase, endonuclease, exonuclease,and/or restriction enzyme).

The polynucleotides fragments (prior to tagging) can comprise sequencesof any length. For example, polynucleotide fragments (prior to tagging)can comprise at least 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105,110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175,180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245,250, 255, 260, 265, 270, 275, 280, 285, 290, 295, 300, 400, 500, 600,700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800,1900, 2000 or more nucleotides in length. The polynucleotide fragmentare preferably about the average length of cell-free DNA. For example,the polynucleotide fragments can comprise about 160 bases in length. Thepolynucleotide fragment can also be fragmented from a larger fragmentinto smaller fragments about 160 bases in length.

Polynucleotides tagged can comprise sequences associated with cancer.The cancer-associated sequences can comprise single nucleotide variation(SNV), copy number variation (CNV), insertions, deletions, and/orrearrangements.

The polynucleotides can comprise sequences associated with cancer, suchas acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML),adrenocortical carcinoma, Kaposi Sarcoma, anal cancer, basal cellcarcinoma, bile duct cancer, bladder cancer, bone cancer, osteosarcoma,malignant fibrous histiocytoma, brain stem glioma, brain cancer,craniopharyngioma, ependymoblastoma, ependymoma, medulloblastoma,medulloeptithelioma, pineal parenchymal tumor, breast cancer, bronchialtumor, Burkitt lymphoma, Non-Hodgkin lymphoma, carcinoid tumor, cervicalcancer, chordoma, chronic lymphocytic leukemia (CLL), chronicmyelogenous leukemia (CML), colon cancer, colorectal cancer, cutaneousT-cell lymphoma, ductal carcinoma in situ, endometrial cancer,esophageal cancer, Ewing Sarcoma, eye cancer, intraocular melanoma,retinoblastoma, fibrous histiocytoma, gallbladder cancer, gastriccancer, glioma, hairy cell leukemia, head and neck cancer, heart cancer,hepatocellular (liver) cancer, Hodgkin lymphoma, hypopharyngeal cancer,kidney cancer, laryngeal cancer, lip cancer, oral cavity cancer, lungcancer, non-small cell carcinoma, small cell carcinoma, melanoma, mouthcancer, myelodysplastic syndromes, multiple myeloma, medulloblastoma,nasal cavity cancer, paranasal sinus cancer, neuroblastoma,nasopharyngeal cancer, oral cancer, oropharyngeal cancer, osteosarcoma,ovarian cancer, pancreatic cancer, papillomatosis, paraganglioma,parathyroid cancer, penile cancer, pharyngeal cancer, pituitary tumor,plasma cell neoplasm, prostate cancer, rectal cancer, renal cell cancer,rhabdomyosarcoma, salivary gland cancer, Sezary syndrome, skin cancer,nonmelanoma, small intestine cancer, soft tissue sarcoma, squamous cellcarcinoma, testicular cancer, throat cancer, thymoma, thyroid cancer,urethral cancer, uterine cancer, uterine sarcoma, vaginal cancer, vulvarcancer, Waldenstrom macroglobulinemia, and/or Wilms Tumor.

In certain embodiments, a population of polynucleotides in a sample offragmented genomic DNA is tagged with n different unique identifiers,wherein n is at least 2 and no more than 100,000*z, wherein z is ameasure of central tendency (e.g., mean, median, mode) of an expectednumber of duplicate molecules having the same start and stop positions.In certain embodiments, n is at least any of 2*z, 3*z, 4*z, 5*z, 6*z,7*z, 8*z, 9*z, 10*z, 11*z, 12*z, 13*z, 14*z, 15*z, 16*z, 17*z, 18*z,19*z, or 20*z (e.g., lower limit). In other embodiments, n is no greaterthan 100,000*z, 10,000*z, 1000*z or 100*z (e.g., upper limit). Thus, ncan range between any combination of these lower and upper limits. Incertain embodiments, n is between 5*z and 15*z, between 8*z and 12*z, orabout 10*z. A haploid human genome equivalent has about 3 picograms ofDNA. A sample of about 1 microgram of DNA contains about 300,000 haploidhuman genome equivalents. Improvements in sequencing can be achieved aslong as at least some of the duplicate or cognate polynucleotides bearunique identifiers with respect to each other, that is, bear differenttags. However, in certain embodiments, the number of tags used isselected so that there is at least a 95% chance that all duplicatemolecules starting at any one position bear unique identifiers. Forexample, in a sample comprising about 10,000 haploid human genomeequivalents of fragmented genomic DNA, e.g., cfDNA, z is expected to bebetween 2 and 8. Such a population can be tagged with between about 10and 100 different identifiers, for example, about 2 identifiers, about 4identifiers, about 9 identifiers, about 16 identifiers, about 25identifiers, about 36 different identifiers, about 49 differentidentifiers, about 64 different identifiers, about 81 differentidentifiers, or about 100 different identifiers.

Nucleic acid barcodes having identifiable sequences including molecularbarcodes, can be used for tagging. For example, a plurality of DNAbarcodes can comprise various numbers of sequences of nucleotides. Aplurality of DNA barcodes having 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 ormore identifiable sequences of nucleotides can be used. When attached toonly one end of a polynucleotide, the plurality of DNA barcodes canproduce 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or more differentidentifiers. Alternatively, when attached to both ends of apolynucleotide, the plurality DNA barcodes can produce 4, 9, 16, 25, 36,49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361, 400 ormore different identifiers (which is the {circumflex over ( )}2 of whenthe DNA barcode is attached to only 1 end of a polynucleotide). In oneexample, a plurality of DNA barcodes having 6, 7, 8, 9 or 10identifiable sequences of nucleotides can be used. When attached to bothends of a polynucleotide, they produce 36, 49, 64, 81 or 100 possibledifferent identifiers, respectively. In a particular example, theplurality of DNA barcodes can comprise 8 identifiable sequences ofnucleotides. When attached to only one end of a polynucleotide, theplurality of DNA barcodes can produce 8 different identifiers.Alternatively, when attached to both ends of a polynucleotide, theplurality of DNA barcodes can produce 64 different identifiers. Samplestagged in such a way can be those with a range of about 10 ng to any ofabout 100 ng, about 1 g, about 10 μg of fragmented polynucleotides,e.g., genomic DNA, e.g., cfDNA.

A polynucleotide can be uniquely identified in various ways. Apolynucleotide can be uniquely identified by a unique DNA barcode. Forexample, any two polynucleotides in a sample are attached two differentDNA barcodes. Alternatively, a polynucleotide can be uniquely identifiedby the combination of a DNA barcode and one or more endogenous sequencesof the polynucleotide. For example, any two polynucleotides in a samplecan be attached the same DNA barcode, but the two polynucleotides canstill be identified by different endogenous sequences. The endogenoussequence can be on an end of a polynucleotide. For example, theendogenous sequence can be adjacent (e.g., base in between) to theattached DNA barcode. In some instances the endogenous sequence can beat least 2, 4, 6, 8, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 bases inlength. Preferably, the endogenous sequence is a terminal sequence ofthe fragment/polynucleotides to be analyzed. The endogenous sequence maybe the length of the sequence. For example, a plurality of DNA barcodescomprising 8 different DNA barcodes can be attached to both ends of eachpolynucleotide in a sample. Each polynucleotide in the sample can beidentified by the combination of the DNA barcodes and about 10 base pairendogenous sequence on an end of the polynucleotide. Without being boundby theory, the endogenous sequence of a polynucleotide can also be theentire polynucleotide sequence.

Also disclosed herein are compositions of tagged polynucleotides. Thetagged polynucleotide can be single-stranded. Alternatively, the taggedpolynucleotide can be double-stranded (e.g., duplex-taggedpolynucleotides). Accordingly, this invention also provides compositionsof duplex-tagged polynucleotides. The polynucleotides can comprise anytypes of nucleic acids (DNA and/or RNA). The polynucleotides compriseany types of DNA disclosed herein. For example, the polynucleotides cancomprise DNA, e.g., fragmented DNA or cfDNA. A set of polynucleotides inthe composition that map to a mappable base position in a genome can benon-uniquely tagged, that is, the number of different identifiers can beat least 2 and fewer than the number of polynucleotides that map to themappable base position. The number of different identifiers can also beat least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25 and fewer than the number of polynucleotides thatmap to the mappable base position.

In some instances, as a composition goes from about 1 ng to about 10 μgor higher, a larger set of different molecular barcodes can be used. Forexample, between 5 and 100 different library adaptors can be used to tagpolynucleotides in a cfDNA sample.

The systems and methods disclosed herein may be used in applicationsthat involve the assignment of molecular barcodes. The molecularbarcodes can be assigned to any types of polynucleotides disclosed inthis invention. For example, the molecular barcodes can be assigned tocell-free polynucleotides (e.g., cfDNAs). Often, an identifier disclosedherein can be a barcode oligonucleotide that is used to tag thepolynucleotide. The barcode identifier may be a nucleic acidoligonucleotide (e.g., a DNA oligonucleotide). The barcode identifiercan be single-stranded. Alternatively, the barcode identifier can bedouble-stranded. The barcode identifier can be attached topolynucleotides using any method disclosed herein. For example, thebarcode identifier can be attached to the polynucleotide by ligationusing an enzyme. The barcode identifier can also be incorporated intothe polynucleotide through PCR. In other cases, the reaction maycomprise addition of a metal isotope, either directly to the analyte orby a probe labeled with the isotope. Generally, assignment of unique ornon-unique identifiers or molecular barcodes in reactions of thisdisclosure may follow methods and systems described by, for example,U.S. patent applications 2001/0053519, 2003/0152490, 2011/0160078 andU.S. Pat. No. 6,582,908, each of which is entirely incorporated hereinby reference.

Identifiers or molecular barcodes used herein may be completelyendogenous whereby circular ligation of individual fragments may beperformed followed by random shearing or targeted amplification. In thiscase, the combination of a new start and stop point of the molecule andthe original intramolecular ligation point can form a specificidentifier.

Identifiers or molecular barcodes used herein can comprise any types ofoligonucleotides. In some cases, identifiers may be predetermined,random, or semi-random sequence oligonucleotides. Identifiers can bebarcodes. For example, a plurality of barcodes may be used such thatbarcodes are not necessarily unique to one another in the plurality.Alternatively, a plurality of barcodes may be used such that eachbarcode is unique to any other barcode in the plurality. The barcodescan comprise specific sequences (e.g., predetermined sequences) that canbe individually tracked. Further, barcodes may be attached (e.g., byligation) to individual molecules such that the combination of thebarcode and the sequence it may be ligated to creates a specificsequence that may be individually tracked. As described herein,detection of barcodes in combination with sequence data of beginning(start) and/or end (stop) portions of sequence reads can allowassignment of a unique identity to a particular molecule. The length ornumber of base pairs of an individual sequence read may also be used toassign a unique identity to such a molecule. As described herein,fragments from a single strand of nucleic acid having been assigned aunique identity, may thereby permit subsequent identification offragments from the parent strand. In this way the polynucleotides in thesample can be uniquely or substantially uniquely tagged. A duplex tagcan include a degenerate or semi-degenerate nucleotide sequence, e.g., arandom degenerate sequence. The nucleotide sequence can comprise anynumber of nucleotides. For example, the nucleotide sequence can comprise1 (if using a non-natural nucleotide), 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,48, 49, 50 or more nucleotides. In a particular example, the sequencecan comprise 7 nucleotides. In another example, the sequence cancomprise 8 nucleotides. The sequence can also comprise 9 nucleotides.The sequence can comprise 10 nucleotides.

A barcode can comprise contiguous or non-contiguous sequences. A barcodethat comprises at least 1, 2, 3, 4, 5 or more nucleotides is acontiguous sequence or non-contiguous sequence. if the 4 nucleotides areuninterrupted by any other nucleotide. For example, if a barcodecomprises the sequence TTGC, a barcode is contiguous if the barcode isTTGC. On the other hand, a barcode is non-contiguous if the barcode isTTXGC, where X is a nucleic acid base.

An identifier or molecular barcode can have an n-mer sequence which maybe 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 or more nucleotides inlength. A tag herein can comprise any range of nucleotides in length.For example, the sequence can be between 2 to 100, 10 to 90, 20 to 80,30 to 70, 40 to 60, or about 50 nucleotides in length.

The tag can comprise a double-stranded fixed reference sequencedownstream of the identifier or molecular barcode. Alternatively, thetag can comprise a double-stranded fixed reference sequence upstream ordownstream of the identifier or molecular barcode. Each strand of adouble-stranded fixed reference sequence can be, for example, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,43, 44, 45, 46, 47, 48, 49, 50 nucleotides in length.

C. Adaptors

A library of polynucleotide molecules can be synthesized for use insequencing. For example, a library of polynucleotides comprising aplurality of polynucleotide molecules that are each less than or equalto 100, 90, 80, 70, 60, 50, 45, 40, or 35 nucleic acid (or nucleotide)bases in length can be made. A plurality of polynucleotide molecules canbe each less than or equal to 35 nucleic acid bases in length. Aplurality of polynucleotide molecules can be each less than or equal to30 nucleic acid bases in length. A plurality of polynucleotide moleculescan also be less than or equal to 250, 200, 150, 100, or 50 nucleic acidbases. Additionally, the plurality of polynucleotide molecules can alsobe less than or equal to 100, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90,89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72,71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54,53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36,35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18,17, 16, 15, 14, 13, 12, 11, or 10 nucleic acid bases.

A library of polynucleotides comprising a plurality of polynucleotidemolecules can also have distinct (with respect to each other) molecularbarcode sequences (or molecular barcodes) with respect to at least 4nucleic acid bases. A molecular barcode (also “barcode” or “identifier”herein) sequence is a nucleotide sequence that distinguishes onepolynucleotide from another. In other embodiments, the polynucleotidemolecules can also have different barcode sequences with respect to 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42, 43, 44, 45, 46, 47, 48, 49, 50 or more nucleic acid bases.

A library of polynucleotides comprising a plurality of polynucleotidemolecules can also have a plurality of different barcode sequences. Forexample, a plurality of polynucleotide molecules can have at least 4different molecular barcode sequences. In some cases, the plurality ofpolynucleotide molecules has from 2-100, 4-50, 4-30, 4-20, or 4-10different molecular barcode sequences. The plurality of polynucleotidesmolecules can also have other ranges of different barcode sequences suchas, 1-4, 2-5, 3-6, 4-7, 5-8, 6-9, 7-10, 8-11, 9-12, 10-13, 11-14, 12-15,13-16, 14-17, 15-18, 16-19, 17-20, 18-21, 19-22, 20-23, 21-24, or 22-25different barcode sequences. In other cases, a plurality ofpolynucleotide molecules can have at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82,83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or100 more different barcode sequences. In a particular example, theplurality library adapters comprise at least 8 different sequences.

The location of the different barcode sequences can vary within theplurality of polynucleotides. For example, the different barcodesequences can be within 20, 15, 10, 9, 8, 7, 6, 5, 4, 3, or 2 nucleicacid bases from a terminal end of a respective one of the plurality ofpolynucleotide molecules. In an example, a plurality of polynucleotidemolecules has distinct barcode sequences that are within 10 nucleic acidbases from the terminal end. In another example, a plurality ofpolynucleotide molecules has distinct barcode sequences that are within5 or 1 nucleic acid bases from the terminal end. In other instances, thedistinct barcode sequences can be at the terminal end of a respectiveone of the plurality of polynucleotide molecules. Other variationsinclude that the distinct molecular barcode sequences can be within 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57,58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93,94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108,109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122,123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164,165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178,179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192,193, 194, 195, 196, 197, 198, 199, 200, or more nucleic acid bases froma terminal end of a respective one of the plurality of polynucleotidemolecules.

The terminal end of the plurality of polynucleotide molecules can beadapted for ligation to a target nucleic acid molecule. For example, theterminal end can be a blunt end. In some other cases, the terminal endis adapted for hybridization to a complementary sequence of a targetnucleic acid molecule.

A library of polynucleotides comprising a plurality of polynucleotidemolecules can also have an edit distance of at least 1. In some cases,the edit distance is with respect to individual bases of the pluralityof polynucleotide molecules. In other cases, the plurality ofpolynucleotide molecules can have an edit distance of at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42, 43, 44, 45, 46, 47, 48, 49, 50 or more. The edit distance can bea Hamming distance.

In some cases, the plurality of polynucleotides does not containsequencing adaptors. A sequence adaptor can be a polynucleotide thatcomprises a sequence that hybridizes to one or more sequencing adaptorsor primers. A sequencing adaptor can further comprise a sequencehybridizing to a solid support, e.g., a flow cell sequence. The term“flow cell sequence” and its grammatical equivalents as used herein,refers to a sequence that permits hybridization to a substrate, forexample, by way of a primer attached to the substrate. The substrate canbe bead or a planar surface. In some embodiments, a flow cell sequencecan allow a polynucleotide to attach to a flow cell or surface (e.g.,surface of a bead, for example, an Illumina flow cell.

When a plurality of polynucleotide molecules does not contain sequencingadaptors or primers, each polynucleotide molecule of the plurality doesnot contain a nucleic acid sequence or other moiety that is adapted topermit sequencing of a target nucleic acid molecule with a givensequencing approach, such as Illumina, SOLiD, Pacific Biosciences,GeneReader, Oxford Nanopore, Complete Genomics, Gnu-Bio, Ion Torrent,Oxford Nanopore or Genia. In some examples, when a plurality ofpolynucleotide molecules does not contain sequencing adaptors orprimers, the plurality of polynucleotide molecules does not contain flowcell sequences. For example, the plurality of polynucleotide moleculescannot bind to flow cells, such as used in Illumina flow cellsequencers. However, these flow cell sequences, if desired, can be addedto the plurality of polynucleotide molecules by methods such as PCRamplification or ligation. At this point, Illumina flow cell sequencerscan be used. Alternatively, when the plurality of polynucleotidemolecules does not contain sequencing adaptors or primers, the pluralityof polynucleotide molecules does not contain hairpin shaped adaptors oradaptors for generating hairpin loops in a target nucleic acid molecule,such as Pacific Bioscience SMRTbell™ adaptors. However, these hairpinshaped adaptors, if desired, can be added to the plurality ofpolynucleotide molecules by methods such as PCR amplification orligation. The plurality of polynucleotide molecules can be circular orlinear.

A plurality of polynucleotide molecules can be double stranded. In somecases, the plurality of polynucleotide molecules can be single stranded,or can comprise hybridized and non-hybridized regions. A plurality ofpolynucleotide molecules can be non-naturally occurring polynucleotidemolecules.

Adaptors can be polynucleotide molecules. The polynucleotide moleculescan be Y-shaped, bubble-shaped or hairpin-shaped. A hairpin adaptor maycontain a restriction site(s) or a Uracil containing base. Adaptors cancomprise a complementary portion and a non-complementary portion. Thenon-complementary portion can have an edit distance (e.g., Hammingdistance). For example, the edit distance can be at least 1, at least 2,at least 3, at least 4, at least 5, at least 6, at least 7, at least 8,at least 9, at least 10, at least 11, at least 12, at least 13, at least14, at least 15, at least 16, at least 17, at least 18, at least 19, atleast 20, at least 21, at least 22, at least 23, at least 24, at least25, at least 26, at least 27, at least 28, at least 29, or at least 30.The complementary portion of the adaptor can comprise sequences that areselected to enable and/or promote ligation to a polynucleotide, e.g., asequence to enable and/or promote ligation to a polynucleotide at a highyield.

A plurality of polynucleotide molecules as disclosed herein can bepurified. In some cases, a plurality of polynucleotide molecules asdisclosed herein can be isolated polynucleotide molecules. In othercases, a plurality of polynucleotide molecules as disclosed herein canbe purified and isolated polynucleotide molecules.

In certain aspects, each of the plurality of polynucleotide molecules isY-shaped or hairpin-shaped. Each of the plurality of polynucleotidemolecules can comprise a different barcode. The different barcode can bea randomer in the complementary portion (e.g., double stranded portion)of the Y-shaped or hairpin-shaped adaptor. Alternatively, the differentbarcode can be in one strand of the non-complementary portion (e.g., oneof the Y-shaped arms). As discussed above, the different barcode can beat least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,19, 20, 21, 22, 23, 24, 25, or more (or any length as describedthroughout) nucleic acid bases, e.g., 7 bases. The barcode can becontiguous or non-contiguous sequences, as described above. Theplurality of polynucleotide molecules is from 10 nucleic acid bases to35 nucleic acid bases (or any length as described above) in length.Further, the plurality of polynucleotide molecules can comprise an editdistance (as described above), that is a Hamming distance. A pluralityof polynucleotide molecules can have distinct barcode sequences that arewithin 10 nucleic acid bases from the terminal end.

In another aspect, a plurality of polynucleotide molecules can besequencing adaptors. A sequencing adaptor can comprise a sequencehybridizing to one or more sequencing primers. A sequencing adaptor canfurther comprise a sequence hybridizing to a solid support, e.g., a flowcell sequence. For example, a sequencing adaptor can be a flow celladaptor. The sequencing adaptors can be attached to one or both ends ofa polynucleotide fragment. In another example, a sequencing adaptor canbe hairpin shaped. For example, the hairpin shaped adaptor can comprisea complementary double-stranded portion and a loop portion, where thedouble-stranded portion can be attached (e.g., ligated) to adouble-stranded polynucleotide. Hairpin shaped sequencing adaptors canbe attached to both ends of a polynucleotide fragment to generate acircular molecule, which can be sequenced multiple times. A sequencingadaptor can be up to 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,95, 96, 97, 98, 99, 100, or more bases from end to end. For example, asequencing adaptor can be up to 70 bases from end to end. The sequencingadaptor can comprise 20-30, 20-40, 30-50, 30-60, 40-60, 40-70, 50-60,50-70, bases from end to end. In a particular example, the sequencingadaptor can comprise 20-30 bases from end to end. In another example,the sequencing adaptor can comprise 50-60 bases from end to end. Asequencing adaptor can comprise one or more barcodes. For example, asequencing adaptor can comprise a sample barcode. The sample barcode cancomprise a pre-determined sequence. The sample barcodes can be used toidentify the source of the polynucleotides. The sample barcode can be atleast 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, or more (or any length as described throughout)nucleic acid bases, e.g., at least 8 bases. The barcode can becontiguous or non-contiguous sequences, as described above.

The plurality of polynucleotide molecules as described herein can beused as adaptors. Adaptors can comprise one or more identifiers. Anadaptor can comprise an identifier with a random sequence.Alternatively, an adaptor can comprise an identifier with pre-determinedsequences. Some adaptors can comprise an identifier with a randomsequence and another identifier with a pre-determined sequence. Theadaptors comprising identifiers can be double-stranded orsingle-stranded adaptors. The adaptors comprising identifiers can beY-shaped adaptors. A Y-shaped adaptor can comprise one or moreidentifiers with a random sequence. The one or more identifiers can beon the hybrid portion and/or non-hybridized portion of the Y-shapedadaptor. A Y-shaped adaptor can comprise one or more identifiers with apre-determined sequence. The one or more identifiers with pre-determinedsequence can be on the hybridized portion and/or non-hybridized portionof the Y-shaped adaptor. A Y-shaped adaptor can comprise one or moreidentifiers with a random sequence and one or more identifiers with apre-determined sequence. For example, the one or more identifiers with arandom sequence can be on the hybridized portion of the Y-shaped adaptorand/or the non-hybridized portion of the Y-shaped adaptor. The one ormore identifiers with a pre-determined sequence can be on the hybridizedportion of the Y-shaped adaptor and/or the non-hybridized portion of theY-shaped adaptor. In a particular example, a Y-shaped adaptor cancomprise an identifier with a random sequence on its hybridized portionand an identifier with a pre-determined sequence on its non-hybridizedportion. The identifiers can be in any length disclosed herein. Forexample, a Y-shaped adaptor can comprise an identifier with a randomsequence of 7 nucleotides on its hybridized portion and an identifierwith a pre-determined sequence of 8 nucleotides on its non-hybridizedportion.

An adaptor can include a double-stranded portion with a molecularbarcode and at least one or two single-stranded portion. For example,the adaptor can be Y-shaped and include a double-stranded portion andtwo single-stranded portions. The single-stranded portions can includesequences that are not complementary to one another.

The adaptor can include a terminal end that has a sequence that isselected to permit the adaptor to be efficiently (e.g., at an efficiencyof at least about 20%, 30%, 40%, 50%) ligated or otherwise coupled to apolynucleotide. In some examples, terminal nucleotides in adouble-stranded portion of an adaptor are selected from a combination ofpurines and pyrimidines to provide for efficient ligation.

In some examples, a set of library adaptors comprises a plurality ofpolynucleotide molecules (library adaptors) with molecular barcodes. Thelibrary adaptors are less than or equal to 80, 70, 60, 50, 45, or 40nucleotide bases in length. The molecular barcodes can be at least 4nucleotide bases in length, but may be from 4 to 20 nucleotide bases inlength. The molecular barcodes can be different from one another andhave an edit distance of at least 1, 2, 3, 4, or 5 between one another.The molecular barcodes are located at least 1, 2, 3, 4, 5, 10, or 20nucleotide bases away from a terminal end of their respective libraryadaptors. In some cases, the at least one terminal base is identical inall of the library adaptors.

The library adaptors can be identical but for the molecular barcodes.For example, the library adaptors can have identical sequences butdiffer only with respect to nucleotide sequences of the molecularbarcodes.

Each of the library adaptors can have a double stranded portion and atleast one single-stranded portion. By “single stranded portion” is meantan area of non-complementarity or an overhang. In some cases, each ofthe library adaptors has a double-stranded portion and twosingle-stranded portions. The double-stranded portion can have amolecular barcode. In some cases, the molecular barcode is a randomer.Each of the library adaptors can further include a strand-identificationbarcode on a single-stranded portion. The strand-identification barcodecan include at least 4 nucleotide bases, in some cases from 4 to 20nucleotide bases.

In some examples, each of the library adaptors has a double-strandedportion with a molecular barcode and two single-stranded portions. Thesingle-stranded portions may not hybridize to one another. Thesingle-stranded portions may not be completely complementary to oneanother.

The library adaptors can have a sequence of terminal nucleotides in adouble-stranded portion that are the same. The sequence of terminalnucleotides can be at least 2, 3, 4, 5 or 6 nucleotide bases in length.For example, one strand of a double-stranded portion of the libraryadaptor can have the sequence ACTT, TCGC, or TACC at the terminal end,while the other strand can have a complementary sequence. In some cases,such a sequence is selected to optimize the efficiency at which thelibrary adaptors ligate to target polynucleotides. Such sequences can beselected to optimize a binding interaction between the ends of thelibrary adaptors and the target polynucleotides.

In some cases, none of the library adaptors contains a sampleidentification motif (or sample molecular barcode). Such sampleidentification motif can be provided via sequencing adaptors. A sampleidentification motif can include a sequencer of at least 4, 5, 6, 7, 8,9, 10, 20, 30, or 40 nucleotide bases that permits the identification ofpolynucleotide molecules from a given sample from polynucleotidemolecules from other samples. For example, this can permitpolynucleotide molecules from two subjects to be sequenced in the samepool and sequence reads for the subjects subsequently identified.

A sequencer motif includes nucleotide sequence(s) needed to couple alibrary adaptor to a sequencing system and sequence a targetpolynucleotide coupled to the library adaptor. The sequencer motif caninclude a sequence that is complementary to a flow cell sequence and asequence (sequencing initiation sequence) that is selectivelyhybridizable to a primer (or priming sequence) for use in sequencing.For example, such sequencing initiation sequence can be complementary toa primer that is employed for use in sequence by synthesis (e.g.,Illumina). Such primer can be included in a sequencing adaptor. Asequencing initiation sequence can be a primer hybridization site.

In some cases, none of the library adaptors contains a completesequencer motif. The library adaptors can contain partial or nosequencer motifs. In some cases, the library adaptors include asequencing initiation sequence. The library adaptors can include asequencing initiation sequence but no flow cell sequence. The sequenceinitiation sequence can be complementary to a primer for sequencing. Theprimer can be a sequence specific primer or a universal primer. Suchsequencing initiation sequences may be situated on single-strandedportions of the library adaptors. As an alternative, such sequencinginitiation sequences may be priming sites (e.g., kinks or nicks) topermit a polymerase to couple to the library adaptors during sequencing.

In some cases, partial or complete sequencer motifs are provided bysequencing adaptors. A sequencing adaptor can include a sample molecularbarcode and a sequencer motif. The sequencing adaptors can be providedin a set that is separate from the library adaptors. The sequencingadaptors in a given set can be identical—i.e., they contain the samesample barcode and sequencer motif.

Sequencing adaptors can include sample identification motifs andsequencer motifs. Sequencer motifs can include primers that arecomplementary to a sequencing initiation sequence. In some cases,sequencer motifs also include flow cell sequences or other sequencesthat permit a polynucleotide to a configured or arranged in a mannerthat permits the polynucleotide to be sequenced by a sequencer.

Library adaptors and sequencing adaptors can each be partial adaptors,that is, containing part but not all of the sequences necessary toenable sequencing by a sequencing platform. Together they providecomplete adaptors. For example, library adaptors can include partial orno sequencer motifs, but such sequencer motifs are provided bysequencing adaptors.

FIGS. 9A-9C schematically illustrate a method for tagging a targetpolynucleotide molecule with library adaptors. FIG. 9A shows a libraryadaptor as a partial adaptor containing a primer hybridization site onone of the strands and a molecular barcode towards another end. Theprimer hybridization site can be a sequencing initiation sequence forsubsequent sequencing. The library adaptor is less than or equal to 80nucleotide bases in length. In FIG. 9B, the library adaptors are ligatedat both ends of the target polynucleotide molecule to provide a taggedtarget polynucleotide molecule. The tagged target polynucleotidemolecule may be subjected to nucleic acid amplification to generatecopies of the target. Next, in FIG. 9C, sequencing adaptors containingsequencer motifs are provided and hybridized to the tagged targetpolynucleotide molecule. The sequencing adaptors contain sampleidentification motifs. The sequencing adaptors can contain sequences topermit sequencing of the tagged target with a given sequencer.

D. Sequencing

Tagged polynucleotides can be sequenced to generate sequence reads(e.g., as shown in step (106), FIG. 1). For example, a tagged duplexpolynucleotide can be sequenced. Sequence reads can be generated fromonly one strand of a tagged duplex polynucleotide. Alternatively, bothstrands of a tagged duplex polynucleotide can generate sequence reads.The two strands of the tagged duplex polynucleotide can comprise thesame tags. Alternatively, the two strands of the tagged duplexpolynucleotide can comprise different tags. When the two strands of thetagged duplex polynucleotide are differently tagged, sequence readsgenerated from one strand (e.g., a Watson strand) can be distinguishedfrom sequence reads generated from the other strands (e.g., a Crickstrand). Sequencing can involve generating multiple sequence reads foreach molecule. This occurs, for example, as a result the amplificationof individual polynucleotide strands during the sequencing process,e.g., by PCR.

Methods disclosed herein can comprise amplifying of polynucleotides.Polynucleotides amplification can result in the incorporation ofnucleotides into a nucleic acid molecule or primer thereby forming a newnucleic acid molecule complementary to a template nucleic acid. Thenewly formed polynucleotide molecule and its template can be used astemplates to synthesize additional polynucleotides. The polynucleotidesbeing amplified can be any nucleic acids, for example, deoxyribonucleicacids, including genomic DNAs, cDNAs (complementary DNA), cfDNAs, andcirculating tumor DNAs (ctDNAs). The polynucleotides being amplified canalso be RNAs. As used herein, one amplification reaction may comprisemany rounds of DNA replication. DNA amplification reactions can include,for example, polymerase chain reaction (PCR). One PCR reaction maycomprise 2-100 “cycles” of denaturation, annealing, and synthesis of aDNA molecule. For example, 2-7, 5-10, 6-11, 7-12, 8-13, 9-14, 10-15,11-16, 12-17, 13-18, 14-19, or 15-20 cycles can be performed during theamplification step. The condition of the PCR can be optimized based onthe GC content of the sequences, including the primers.

Nucleic acid amplification techniques can be used with the assaysdescribed herein. Some amplification techniques are the PCRmethodologies which can include, but are not limited to, solution PCRand in situ PCR. For example, amplification may comprise PCR-basedamplification. Alternatively, amplification may comprise non PCR-basedamplification. Amplification of the template nucleic acid may compriseuse of one or more polymerases. For example, the polymerase may be a DNApolymerase or an RNA polymerase. In some cases, high fidelityamplification is performed such as with the use of high fidelitypolymerase (e.g., Phusion® High-Fidelity DNA Polymerase) or PCRprotocols. In some cases, the polymerase may be a high fidelitypolymerase. For example, the polymerase may be KAPA HiFi DNA polymerase.The polymerase may also be Phusion DNA polymerase. The polymerase may beused under reaction conditions that reduce or minimize amplificationbiases, e.g., due to fragment length, GC content, etc.

Amplification of a single strand of a polynucleotide by PCR willgenerate copies both of that strand and its complement. Duringsequencing, both the strand and its complement will generate sequencereads. However, sequence reads generated from the complement of, forexample, the Watson strand, can be identified as such because they bearthe complement of the portion of the duplex tag that tagged the originalWatson strand. In contrast, a sequence read generated from a Crickstrand or its amplification product will bear the portion of the duplextag that tagged the original Crick strand. In this way, a sequence readgenerated from an amplified product of a complement of the Watson strandcan be distinguished from a complement sequence read generated from anamplification product of the Crick strand of the original molecule.

All amplified polynucleotides can be submitted to a sequencing devicefor sequencing. Alternatively, a sampling, or subset, of all of theamplified polynucleotides is submitted to a sequencing device forsequencing. With respect to any original double-stranded polynucleotidethere can be three results with respect to sequencing. First, sequencereads can be generated from both complementary strands of the originalmolecule (that is, from both the Watson strand and from the Crickstrand). Second, sequence reads can be generated from only one of thetwo complementary strands (that is, either from the Watson strand orfrom the Crick strand, but not both). Third, no sequence read may begenerated from either of the two complementary strands. Consequently,counting unique sequence reads mapping to a genetic locus willunderestimate the number of double-stranded polynucleotides in theoriginal sample mapping to the locus. Described herein are methods ofestimating the unseen and uncounted polynucleotides.

The sequencing method can be massively parallel sequencing, that is,simultaneously (or in rapid succession) sequencing any of at least 100,1000, 10,000, 100,000, 1 million, 10 million, 100 million, or 1 billionpolynucleotide molecules. Sequencing methods may include, but are notlimited to: high-throughput sequencing, pyrosequencing,sequencing-by-synthesis, single-molecule sequencing, nanoporesequencing, semiconductor sequencing, sequencing-by-ligation,sequencing-by-hybridization, RNA-Seq (Illumina), Digital Gene Expression(Helicos), Next generation sequencing, Single Molecule Sequencing bySynthesis (SMSS)(Helicos), massively-parallel sequencing, Clonal SingleMolecule Array (Solexa), shotgun sequencing, Maxam-Gilbert or Sangersequencing, primer walking, sequencing using PacBio, SOLiD, Ion Torrent,or Nanopore platforms and any other sequencing methods known in the art.

For example, duplex-tagged polynucleotides can be amplified, by forexample PCR (see e.g., FIG. 4A duplex-tagged polynucleotides arereferred to as mm’ and nn′). In FIG. 4A, the strand of the duplexpolynucleotide including sequence m bears sequence tags w and y, whilethe strand of the duplex polynucleotide including sequence m′ bearssequence tags x and z. Similarly, the strand of the duplexpolynucleotide including sequence n bears sequence tags a and c, whilethe strand of the duplex polynucleotide including sequence n′ bearssequence tags b and d. During amplification, each strand produces itselfand its complementary sequence. However, for example, an amplificationprogeny of original strand m that includes the complementary sequence,m′, is distinguishable from an amplification progeny of original strandm′ because the progeny from original strand m will have the sequence5′-y′m′w′-3′ and the progeny of the original m′ strand one strand willhave the sequence 5′-zm′x-3′. FIG. 4B shows amplification in moredetail. During amplification, errors can be introduced into theamplification progeny, represented by dots. The application progeny aresampled for sequencing, so that not all strands produce sequence reads,resulting in the sequence reads indicated. Because sequence reads cancome from either of a strand or its complement, both sequences andcomplement sequences will be included in the set of sequence reads. Itshould be noted that it is possible that a polynucleotide would bear thesame tag on each end. Thus, for a tag “a”, and polynucleotide “m”, afirst strand could be tagged a-m-a′, and the complement could be taggeda-m′-a.

E. Determining Consensus Sequence Reads

Methods disclosed herein can comprise determining consensus sequencereads in sequence reads (e.g., as shown in step (108), FIG. 1), such asby reducing or tracking redundancy. Sequencing of amplifiedpolynucleotides can produce reads of the several amplification productsfrom the same original polynucleotide, referred to as “redundant reads”.By identifying redundant reads, unique molecules in the original samplecan be determined. If the molecules in a sample are uniquely tagged,then reads generated from amplification of a single unique originalmolecule can be identified based on their distinct barcode. Ignoringbarcodes, reads from unique original molecules can be determined basedon sequences at the beginning and end of a read, optionally incombination with the length of the read. In certain cases, however, asample may be expected to have a plurality of original molecules havingthe same start stop sequences and the same length. Without barcoding,these molecules are difficult to distinguish from one another. However,if a collection of polynucleotides is non-uniquely tagged (that is, anoriginal molecule shares the same identifier with at least one otheroriginal molecule), combining information from a barcode with start/stopsequence and/or polynucleotide length significantly increases theprobability that any sequence read can be traced back to an originalpolynucleotide. This is because, in part, even without unique tagging,it is unlikely that any two original polynucleotides having the samestart/stop sequence and length also will be tagged with the sameidentifier.

F. Collapsing

Collapsing allows for reduction in noise (i.e., background) that isgenerated at each step of the process. Methods disclosed herein cancomprise collapsing, e.g., generating a consensus sequence by comparingmultiple sequence reads. For example, sequence reads generated from asingle original polynucleotide can be used to generate a consensussequence of that original polynucleotide. Iterative rounds ofamplification can introduce errors into progeny polynucleotides. Also,sequencing typically may not be performed with perfect fidelity sosequencing errors are introduced at this stage as well. However,comparison of sequence reads of molecules derived from a single originalmolecule, including those that have sequence variants, can be analyzedso as to determine the original, or “consensus” sequence. This can bedone phylogenetically. Consensus sequences can be generated fromfamilies of sequence reads by any of a variety of methods. Such methodsinclude, for example, linear or non-linear methods of building consensussequences (such as voting (e.g., biased voting), averaging, statistical,maximum a posteriori or maximum likelihood detection, dynamicprogramming, Bayesian, hidden Markov or support vector machine methods,etc.) derived from digital communication theory, information theory, orbioinformatics. For example, if all or most of the sequence readstracking back to an original molecule bear the same sequence variant,that variant probably existed in the original molecule. On the otherhand, if a sequence variant exists in a subset of redundant sequencereads, that variant may have been introduced duringamplification/sequencing and represents an artifact not existing in theoriginal. Furthermore, if only sequence reads derived from the Watson orCrick strand of an original polynucleotide contain the variant, thevariant may have been introduced through single-sided DNA damage,first-cycle PCR error or through contaminating polynucleotides that wereamplified from a different sample.

After fragments are amplified and the sequences of amplified fragmentsare read and aligned, the fragments are subjected to base calling, e.g.,determining for each locus the most likely nucleotide. However,variations in the number of amplified fragments and unseen amplifiedfragments (e.g., those without being read their sequences; reasons couldbe too many such as amplification errors, sequencing reading errors, toolong, too short, being chopped, etc.) may introduce errors in basecalling. If there are too many unseen amplified fragments with respectto the seen amplified fragments (amplified fragments actually beingread), the reliability of base calling may be diminished.

Therefore, disclosed herein is a method to correct for the number ofunseen fragments in base calling. For example, when base calling forlocus A (an arbitrary locus), it is first assumed that there are Namplified fragments. The sequence readouts can come from two types offragments: double-strand fragments and single-strand fragments.Therefore, we assign N1, N2, and N3 as the numbers of double-strands,single-strands, and unseen fragments, respectively. Thus, N=N1+N2+N3 (N1and N2 are known from the sequence readouts, and N and N3 are unknown).If the formula is solved for N (or N3), then N3 (or N) will be inferred.

Probability is used to estimate N. For example, we assign “p” to be theprobability of having detected (or having read) a nucleotide of locus Ain a sequence readout of a single-strand.

For sequence readouts from double-strands, the nucleotide call from adouble-strand amplified fragment has a probability of p*p=p{circumflexover ( )}2, seeing all N1 double-strands has the following equation:N1=N*(p{circumflex over ( )}2).

For sequence readouts from a single-strand. Assuming that one of the 2strands is seen, and the other is unseen, the probability of seeing onestrand is “p”, but the probability of missing the other strand is (1−p).Furthermore, by not distinguishing the single strand sourcing from5-primer and sourcing from 3-primer, there is a factor of 2. Therefore,the nucleotide call from a single-strand amplified fragment has aprobability 2×p×(1−p). Thus, seeing all N2 single-strands has thefollowing equation: N2=N×2×p×(1−p).

“p” is also unknown. To solve p, the ratio of N1 to N2 is used to solvefor “p”:

$R = {\frac{N1}{N2} = {\frac{{Np}^{2}}{2{{Np}\left( {1 - p} \right)}} = {\frac{p^{2}}{2{p\left( {1 - p} \right)}} = \frac{p}{2\left( {1 - p} \right)}}}}$

Once “p” is found, N can be found. After N is found, can be foundN3=N−N1−N2.

Besides the ratio of paired versus unpaired strands (which is a measurepost-collapsing), there is useful information in the pre-collapsing readdepth at each locus. This information can be used to further improve thecall for total molecule count and/or increase confidence of callingvariants.

For example, FIG. 4C demonstrates sequence reads corrected forcomplementary sequences. Sequences generated from an original Watsonstrand or an original Crick strand can be differentiated on the basis oftheir duplex tags. Sequences generated from the same original strand canbe grouped. Examination of the sequences can allow one to infer thesequence of the original strand (the “consensus sequence”). In thiscase, for example, the sequence variant in the nn′ molecule is includedin the consensus sequence because it included in every sequence readwhile other variants are seen to be stray errors. After collapsingsequences, original polynucleotide pairs can be identified based ontheir complementary sequences and duplex tags.

FIG. 5 demonstrates increased confidence in detecting sequence variantsby pairing reads from Watson and Crick strands. Sequence nn′ can includea sequence variant indicated by a dot. In some cases, sequence pp′ doesnot include a sequence variant. Amplification, sequencing, redundancyreduction and pairing can result in both Watson and Crick strands of thesame original molecule including the sequence variant. In contrast, as aresult of errors introduced during amplification and sampling duringsequencing, the consensus sequence of the Watson strand p can contain asequence variant, while the consensus sequence of the Crick strand p′does not. It is less likely that amplification and sequencing willintroduce the same variant into both strands (nn′ sequence) of a duplexthan onto one strand (pp′ sequence). Therefore, the variant in the pp′sequence is more likely to be an artifact, and the variant in the nn′sequence is more likely to exist in the original molecule.

Methods disclosed herein can be used to correct errors resulted fromexperiments, e.g., PCR, amplification, and/or sequencing. For example,such a method can comprises attaching one or more double strandedadaptors to both ends of a double stranded polynucleotide, therebyproviding a tagged double stranded polynucleotide; amplifying the doublestranded tagged polynucleotide; sequencing both strands of the taggedpolynucleotide; comparing the sequence of one strand with its complementto determine any errors introduced during sequencing; and correctingerrors in the sequence based on (d). The adaptors used in this methodcan be any adaptors disclosed herein, e.g., Y-shaped adaptors. Theadaptor can comprise any barcodes (e.g., distinct barcodes) disclosedherein.

G. Manning

Sequence reads or consensus sequences can be mapped to one or moreselected genetic loci (e.g., as shown step (110), FIG. 1). A geneticlocus can be, for example, a specific nucleotide position in the genome,a sequence of nucleotides (for example, an open reading frame), afragment of a chromosome, a whole chromosome, or an entire genome. Agenetic locus can be a polymorphic locus. Polymorphic locus can be alocus at which sequence variation exists in the population and/or existsin a subject and/or a sample. A polymorphic locus can be generated bytwo or more distinct sequences coexisting at the same location of thegenome. The distinct sequences can differ from one another by one ormore nucleotide substitutions, a deletion/insertion, and/or aduplication of any number of nucleotides, generally a relatively smallnumber of nucleotides, such as less than 50, 45, 40, 35, 30, 25, 24, 23,22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3,2, or 1 nucleotide(s), among others. A polymorphic locus can be createdby a single nucleotide position that varies within the population, e.g.a single nucleotide variation (SNV) or a single nucleotide polymorphism(SNP).

A reference genome for mapping can include the genome of any species ofinterest. Human genome sequences useful as references can include thehg19 assembly or any previous or available hg assembly. Such sequencescan be interrogated using the genome browser available atgenome.ucsc.edu/index.html. Other species genomes include, for examplePanTro2 (chimp) and mm9 (mouse).

In methods disclosed herein, collapsing can be performed before or aftermapping. In some aspects, collapsing can be performed before mapping.For example, sequence reads can be grouped into families based on theirtags and one or more endogenous sequences, without regard to where thereads map in the genome. Then, the members of a family can be collapsedinto a consensus sequence. The consensus sequence can be generated usingany collapsing method disclosed herein. Then the consensus sequence canbe mapped to locations in the genome. Reads mapped to a locus can bequantified (e.g., counted). Percentage of reads carrying a mutation at alocus can also be determined. Alternatively, collapsing can be performedafter mapping. For example, all reads can first be mapped to the genome.Then the reads can be grouped into families based on their tags and oneor more endogenous sequences. Since the reads have been mapped to thegenome, consensus bases can be determined for each family at each locus.In other aspects, consensus sequence can be generated for one strand ofa DNA molecule (e.g., for a Watson strand or a Crick strand). Mappingcan be performed before or after the consensus sequence for one strandof the DNA molecule is determined. Numbers of Doublets and Singlets canbe determined. These numbers can be used to calculate unseen molecules.For example, the unseen molecules can be calculated using the followingequation: N=D+S+U; D=Np(2), S=N2pq, where p=1−q, where p is theprobability of seeing; q is the probability of missing a strand.

H. Grouping

Methods disclosed herein can also comprise grouping sequence reads.Sequence reads can be grouped based on various types of sequences, e.g.,sequences of an oligonucleotide tag (e.g., a barcode), sequence of apolynucleotide fragments, or any combinations. For example, as shown instep (112) (FIG. 1), sequence reads can be grouped as follows: Sequencereads generated from a “Watson” strand and those generated from a“Crick” strand of a double-stranded polynucleotide in the sample areidentifiable based on the duplex tags that they bear. In this way, asequence read or consensus sequence from a Watson strand of a duplexpolynucleotide can be paired with a sequence read or consensus sequencefrom its complementary Crick strand. Paired sequence reads are referredto as a “Pair”.

Sequence reads for which no sequence read corresponding to acomplementary strand can be found among the sequence reads are termed“Singlets”.

Double-stranded polynucleotides for which a sequence read for neither ofthe two complementary strands has been generated are referred to as“Unseen” molecules.

I. Quantifying

Methods disclosed herein also comprise quantifying sequence reads. Forexample, as shown in step (114) (FIG. 1), Pairs and Singlets mapping toa selected genetic locus, or to each of a plurality of selected geneticloci, are quantified, e.g., counted.

The quantifying can comprise estimating number of polynucleotides in thesample (e.g., Pairs polynucleotides, Singlets polynucleotides, or Unseenpolynucleotides. For example, as shown in step (116) (FIG. 1), thenumber of double-stranded polynucleotides in the sample for which nosequence reads were generated (“Unseen” polynucleotides) is estimated.The probability that a double strand polynucleotide generates nosequence reads can be determined based on the relative number of Pairsand Singlets at any locus. Using this probability, the number of Unseenpolynucleotide can be estimated.

In step (118) an estimate for the total number of double-strandedpolynucleotides in a sample mapping to a selected locus is the sum ofthe number of Pairs, the number of Singlets and the number of Unseenmolecules mapping to the locus.

The number of Unseen original molecules in a sample can be estimatedbased on the relative number of Pairs and Singlets (FIG. 2). Referringto FIG. 2, as an example, counts for a particular genomic locus, LocusA, are recorded, where 1000 molecules are paired and 1000 molecules areunpaired. Assuming a uniform probability, p, for an individual Watson orCrick strand to make it through the process subsequent to conversion,one can calculate the proportion of molecules that fail to make itthrough the process (Unseen) as follows: Let R=ratio of paired tounpaired molecules=1, so R=1=p²/(2p(1−p)). This implies that p=⅔ andthat the quantity of lost molecules is equal to (1−p)²= 1/9. Thus inthis example, approximately 11% of converted molecules are lost andnever detected. Consider another genomic locus, Locus B, in the samesample where 1440 molecules are paired and 720 are unpaired. Using thesame method, we can infer the number of molecules that are lost, is only4%. Comparing the two areas, it may be assumed that Locus A had 2000unique molecules as compared to 2160 molecules in Locus B−a differenceof almost 8%. However, by correctly adding in the lost molecules in eachregion, we infer there are 2000/( 8/9)=2250 molecules in Locus A and2160/0.96=2250 molecules in Locus B. Hence, the counts in both regionsare actually equal. This correction and thus much higher sensitivity canbe achievable by converting the original double-stranded nucleic acidmolecules and bioinformatically keeping track of all those that arepaired and unpaired at the end of the process. Similarly, the sameprocedure can be used to infer true copy number variations in regionsthat appear to have similar counts of observed unique molecules. Bytaking the number of unseen molecules into consideration in the two ormore regions, the copy number variation becomes apparent.

In addition to using binomial distribution, other methods of estimatingnumbers of unseen molecules include exponential, beta, gamma orempirical distributions based on the redundancy of sequence readsobserved. In the latter case, the distribution of read counts for pairedand unpaired molecules can be derived from such redundancy to infer theunderlying distribution of original polynucleotide molecules at aparticular locus. This can often lead to a better estimation of thenumber of unseen molecules.

J. CNV Detection

Methods disclosed herein also comprise detecting CNV. For example, asshown in step (120) (FIG. 1), once the total number of polynucleotidesmapping to a locus is determined, this number can be used in standardmethods of determining CNV at the locus. A quantitative measure can benormalized against a standard. The standard can be an amount of anypolynucleotides. In one method, a quantitative measure at a test locuscan be standardized against a quantitative measure of polynucleotidesmapping to a control locus in the genome, such as gene of known copynumber. Quantitative measures can be compared against the amount ofnucleic acid in any sample disclosed herein. For example, in anothermethod, the quantitative measure can be compared against the amount ofnucleic acid in the original sample. For example, if the original samplecontained 10,000 haploid gene equivalents, the quantitative measure canbe compared against an expected measure for diploidy. In another method,the quantitative measure can be normalized against a measure from acontrol sample, and normalized measures at different loci can becompared.

In some cases, in which copy number variation analysis is desired,sequence data may be: 1) aligned with a reference genome; 2) filteredand mapped; 3) partitioned into windows or bins of sequence; 4) coveragereads counted for each window; 5) coverage reads can then be normalizedusing a stochastic or statistical modeling algorithm; 6) and an outputfile can be generated reflecting discrete copy number states at variouspositions in the genome. In other cases, in which rare mutation analysisis desired, sequence data may be 1) aligned with a reference genome; 2)filtered and mapped; 3) frequency of variant bases calculated based oncoverage reads for that specific base; 4) variant base frequencynormalized using a stochastic, statistical or probabilistic modelingalgorithm; 5) and an output file can be generated reflecting mutationstates at various positions in the genome.

After the sequence read coverage ratios have been determined, astochastic modeling algorithm can be optionally applied to convert thenormalized ratios for each window region into discrete copy numberstates. In some cases, this algorithm may comprise a Hidden MarkovModel. In other cases, the stochastic model may comprise dynamicprogramming, support vector machine, Bayesian modeling, probabilisticmodeling, trellis decoding, Viterbi decoding, expectation maximization,Kalman filtering methodologies, or neural networks.

Methods disclosed herein can comprise detecting SNVs, CNVs, insertions,deletions, and/or rearrangements at a specific region in a genome. Thespecific genomic region can comprise a sequence in a gene, such as ALK,APC, BRAF, CDKN2A, EGFR, ERBB2, FBXW7, KRAS, MYC, NOTCH1, NRAS, PIK3CA,PTEN, RB1, TP53, MET, AR, ABL1, AKT1, ATM, CDH1, CSF1R, CTNNB1, ERBB4,EZH2, FGFR1, FGFR2, FGFR3, FLT3, GNA11, GNAQ, GNAS, HNF1A, HRAS, IDH1,IDH2, JAK2, JAK3, KDR, KIT, MLH1, MPL, NPM1, PDGFRA, PROC, PTPN11, RET,SMAD4, SMARCB1, SMO, SRC, STK11, VHL, TERT, CCND1, CDK4, CDKN2B, RAF1,BRCA1, CCND2, CDK6, NF1, TP53, ARID1A, BRCA2, CCNE1, ESR1, RIT1, GATA3,MAP2K1, RHEB, ROS1, ARAF, MAP2K2, NFE2L2, RHOA, or NTRK1.

In some cases, the method uses a panel which comprises exons of one ormore genes. The panel can comprise introns of one or more genes as well.The panel can also comprise exons and introns of one or more genes. Theone or more genes can be those disclosed above. The panel can compriseabout 80,000 bases which cover a panel of genes. The panel can compriseabout 1000, 2000, 3000, 4000, 5000, 10000, 15000, 20000, 25000, 30000,35000, 40000, 45000, 50000, 55000, 60000, 65000, 70000, 75000, 80000,85000, 90000, 95000, 100000, 105000, 110000, 115000, 120000, 125000, ormore bases.

In some aspects, copy number of a gene can be reflected in the frequencyof a genetic form of the gene in a sample. For example, in a healthyindividual, no copy number variation is reflected in a variant in a genein one chromosome (e.g., heterozygosity) being detected in about 50% ofdetected molecules in a sample. Also, in a healthy individual,duplication of a gene bearing a variant can be reflected in the variantbeing detected in about 66% of detected molecules in a sample.Accordingly, if the tumor burden in a DNA sample is 10%, the frequencyof a somatic mutation in a gene in one chromosome of cancer cells,without CNV, can be about 5%. The converse can be true in the case ofaneuploidy.

The methods disclosed herein can be used to determine whether a sequencevariant is more likely present in the germ line level or resulted from asomatic cell mutation, e.g., in a cancer cell. For example, a sequencevariant in a gene detected at levels arguably consistent withheterozygosity in the germ line is more likely the product of a somaticmutation if CNV is also detected in that gene. In some cases, to theextent we expect that a gene duplication in the germ line bears avariant consistent with genetic dose (e.g., 66% for trisomy at a locus),detection gene amplification with a sequence variant dose that deviatessignificantly from this expected amount indicates that the CNV is morelikely present as a result of somatic cell mutation.

The methods disclosed herein can also be used to infer tumorheterogeneity in a situation in which sequence variants in two genes aredetected at different frequencies. For example, tumor heterogeneity canbe inferred when two genes are detected at different frequencies buttheir copy numbers are relatively equal. Alternatively, tumorhomogeneity can be inferred when the difference in frequency between twosequence variants is consistent with difference in copy number for thetwo genes. Thus, for example, if an EGFR variant is detected at 11% anda KRAS variant is detected at 5%, and no CNV is detected at these genes,the difference in frequency likely reflects tumor heterogeneity (e.g.,all tumor cells carry an EGFR mutant and half the tumor cells also carrya KRAS mutant). Alternatively, if the EGFR gene carrying the mutant isdetected at 2-times normal copy number, one interpretation is ahomogenous population of tumor cells, each cell carrying a mutant in theEGFR and KRAS genes, but in which the KRAS gene is duplicated.

In response to chemotherapy, a dominant tumor form can eventually giveway through Darwinian selection to cancer cells carrying mutants thatrender the cancer unresponsive to the therapy regimen. Appearance ofthese resistance mutants can be delayed through methods of thisinvention. In one embodiment of this method, a subject is subjected toone or more pulsed therapy cycles, each pulsed therapy cycle comprisinga first period during which a drug is administered at a first amount anda second cycle during which the drug is administered at a second,reduced amount. The first period can be characterized by a tumor burdendetected above a first clinical level. The second period can becharacterized by a tumor burden detected below a second clinical level.First and second clinical levels can be different in different pulsedtherapy cycles. For example, the first clinical level can be lower insucceeding cycles. A plurality of cycles can include at least 2, 3, 4,5, 6, 7, 8 or more cycles. For example, the BRAF mutant V600E may bedetected in polynucleotides of a disease cell at an amount indicating atumor burden of 5% in cfDNA. Chemotherapy can commence with dabrafenib.Subsequent testing can show that the amount of the BRAF mutant in thecfDNA falls below 0.5% or to undetectable levels. At this point,dabrafenib therapy can stop or be significantly curtailed. Furthersubsequent testing may find that DNA bearing the BRAF mutation has risento 2.5% of polynucleotides in cfDNA. At this point, dabrafenib therapycan be re-started, e.g., at the same level as the initial treatment.Subsequent testing may find that DNA bearing the BRAF mutation hasdecreased to 0.5% of polynucleotides in cfDNA. Again, dabrafenib therapycan be stopped or reduced. The cycle can be repeated a number of times.

A therapeutic intervention can also be changed upon detection of therise of a mutant form resistant to an original drug. For example,cancers with the EGFR mutation L858R respond to therapy with erlotinib.However, cancers with the EGFR mutation T790M are resistant toerlotinib. However, they are responsive to ruxolitinib. A method of thisinvention involves monitoring changes in tumor profile and changing atherapeutic intervention when a genetic variant associated with drugresistance rises to a predetermined clinical level.

Methods disclosed in this invention can comprise a method of detectingdisease cell heterogeneity from a sample comprising polynucleotides fromsomatic cells and disease cells, the method comprising: a) quantifyingpolynucleotides in the sample bearing a sequence variant at each of aplurality of genetic loci; b) determining CNV at each of the pluralityof genetic loci; different relative amounts of disease molecules at alocus, wherein the CNV indicates a genetic dose of a locus in thedisease cell polynucleotides; c) determining a relative measure ofquantity of polynucleotides bearing a sequence variant at a locus pergenetic dose at the locus for each of a plurality of the loci; and d)comparing the relative measures at each of the plurality of loci,wherein different relative measures indicates tumor heterogeneity. Inthe methods disclosed herein, the genetic dose can be determined on atotal molecule basis. For example, if there are 1× total molecules at afirst locus, and 1.2× molecules mapped to a second locus, then thegenetic dose is 1.2. Variants at this locus can be divided by 1.2. Insome aspects, the method disclosed herein can be used to detect anydisease cell heterogeneity, e.g., tumor cell heterogeneity. The methodscan be used to detect disease cell heterogeneity from a samplecomprising any types of polynucleotides, e.g., cfDNA, genomic DNA, cDNA,or ctDNA. In the methods, the quantifying can comprise, for example,determining the number or relative amount of the polynucleotides.Determining CNV can comprise mapping and normalizing different relativeamounts of total molecules to a locus.

In another aspect, in response to chemotherapy, a dominant tumor formcan eventually give way through Darwinian selection to cancer cellscarrying mutants that render the cancer unresponsive to the therapyregimen. Appearance of these resistance mutants can be delayed throughmethods disclosed throughout. The methods disclosed herein can comprisea method comprising: a) subjecting a subject to one or more pulsedtherapy cycles, each pulsed therapy cycle comprising (i) a first periodduring which a drug is administered at a first amount and (ii) a secondperiod during which the drug is administered at a second, reducedamount; wherein (A) the first period is characterized by a tumor burdendetected above a first clinical level; and (B) the second period ischaracterized by a tumor burden detected below a second clinical level.

K. Sequence Variant Detection

Systems and methods disclosed herein can be used to detect sequencevariants, e.g., SNVs. For example, a sequence variant can be detectedfrom consensus sequences from multiple sequence reads, for example, fromat least 2, at least 3, at least 4, at least 5, at least 6, at least 7,at least 8, at least 9, at least 10, at least 11, at least 12, at least13, at least 14, at least 15, at least 16, at least 17, at least 18, atleast 19, at least 20, at least 21, at least 22, at least 23, at least24, at least 25, at least 26, at least 27, at least 28, at least 29, atleast 30, at least 31, at least 32, at least 33, at least 34, at least35, at least 36, at least 37, at least 38, at least 39, at least 40, atleast 41, at least 42, at least 43, at least 44, at least 45, at least46, at least 47, at least 48, at least 49, at least 50, at least 51, atleast 52, at least 53, at least 54, at least 55, at least 56, at least57, at least 58, at least 59, at least 60, at least 61, at least 62, atleast 63, at least 64, at least 65, at least 66, at least 67, at least68, at least 69, at least 70, at least 71, at least 72, at least 73, atleast 74, at least 75, at least 76, at least 77, at least 78, at least79, at least 80, at least 81, at least 82, at least 83, at least 84, atleast 85, at least 86, at least 87, at least 88, at least 89, at least90, at least 91, at least 92, at least 93, at least 94, at least 95, atleast 96, at least 97, at least 98, at least 99, at least 100, at least200, at least 300, at least 400, at least 500, at least 600, at least700, at least 800, at least 900, at least 1000, at least 2000, at least3000, at least 4000, at least 5000, at least 6000, at least 7000, atleast 8000, at least 9000, at least 10000 or more sequence reads. Aconsensus sequence can be from sequence reads of a single strandpolynucleotide. A consensus sequence can also be from sequence reads ofone strand of a double-stranded polynucleotide (e.g., pairing reads). Inan exemplary method, pairing reads allows one to identify with increasedconfidence the existence of a sequence variant in a molecule. Forexample, if both strands of a Pair include the same variant, one can bereasonably sure that the variant existed in the original molecule, asthe chance that the same variant is introduced into both strands duringamplification/sequencing is rare. In contrast, if only one strand of aPair includes the sequence variant, this is more likely to be anartifact. Similarly, the confidence that a Singlet bearing a sequencevariant existed in the original molecule is less than the confidence ifthe variant exists in a Duplex, as there is higher probability that thevariant can be introduced once than twice duringamplification/sequencing.

Other methods of copy number variation detection and the sequencevariant detection are described in PCT/US2013/058061, which is entirelyincorporated herein by reference.

Sequence reads can be collapsed to generate a consensus sequence, whichcan be mapped to a reference sequence to identify genetic variants, suchas CNV or SNV. As an alternative, the sequence reads are mapped prior toor even without mapping. In such a case, the sequence reads can beindividually mapped to the reference to identify a CNV or SNV.

FIG. 3 shows a reference sequence encoding a genetic Locus A. Thepolynucleotides in FIG. 3 may be Y-shaped or have other shapes, such ashairpin.

In some cases, an SNV or multiple-nucleotide variant (MNV) can bedetermined across multiple sequence reads at a given locus (e.g.,nucleotide base) by aligning sequence reads that correspond to thatlocus. Next, a plurality of sequential nucleotide bases from at least asubset of the sequence reads are mapped to the reference to a SNV or MNVin a polynucleotide molecule or portion thereof that corresponds to thereads. The plurality of sequential nucleotide bases can span an actual,inferred or suspected location of the SNV or MNV. The plurality ofsequential nucleotide bases can span at least 3, 4, 5, 6, 7, 8, 9, or 10nucleotide bases.

L. Detecting/Quantifying Nucleic Acids

The methods described throughout can be used to tag nucleic acidsfragments, such as deoxyribonucleic acid (DNA), at extremely highefficiency. This efficient tagging allows a person to efficiently andaccurately detect rare DNA in heterogenous populations of original DNAfragments (such as in cfDNA). A rare polynucleotide (e.g., rare DNA) canbe a polynucleotide that comprises a genetic variant occurring in apopulation of polynucleotides at a frequency of less than 10%, 5%, 4%,3%, 2%, 1%, or 0.1%. A rare DNA can be a polynucleotide with adetectable property at a concentration less than 50%, 25%, 10%, 5%, 1%,or 0.1%

Tagging can occur in a single reaction. In some cases, two or morereactions can be performed and pooled together. Tagging each originalDNA fragments in a single reaction can result in tagging such thatgreater than 50% (e.g., 60%, 70%, 80%, 90%, 95%, or 99%) of the originalDNA fragments are tagged at both ends with tags that comprise molecularbarcodes, thereby providing tagged DNA fragments. Tagging can alsoresult in greater than 30%, 35%, 40%, 45%, 50%, 51%, 52%, 53%, 54%, 55%,56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%,70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%,84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,98%, or 99% of the original DNA fragments tagged at both ends with tagsthat comprise molecular barcodes. Tagging can also result in 100% of theoriginal DNA fragments tagged at both ends with tags that comprisemolecular barcodes. Tagging can also result in single end tagging.

Tagging can also occur by using an excess amount of tags as compared tothe original DNA fragments. For example, the excess can be at least5-fold excess. In other cases, the excess can be at least 1.25, 1.5,1.75, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,90, 95, 100 or more fold excess. Tagging can comprise attachment toblunt ends or sticky ends. Tagging can also be performed byhybridization PCR. Tagging can also be performed in low reactionvolumes, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 pico- and/ormicroliters.

The method can also include performing high fidelity amplification onthe tagged DNA fragments. Any high fidelity DNA polymerases can be used.For example, the polymerase may be KAPA HiFi DNA polymerase or PhusionDNA polymerase.

Further, the method can comprise selectively enriching a subset of thetagged DNA fragments. For example, selective enrichment can be performedby hybridization or amplification techniques. The selective enrichmentcan be performed using a solid support (e.g., beads). The solid support(e.g., beads) can comprise probes (e.g., oligonucleotides specificallyhybridizing to certain sequences. For example, the probes can hybridizewith certain genomic regions, e.g., genes. In some cases, the genomicregions, e.g., genes, can be regions associated with diseases, e.g.,cancer. After enrichment, the selected fragmented can be attached anysequencing adaptor disclosed in this invention. For example, a sequenceadaptor can comprise a flow cell sequence, a sample barcode, or both. Inanother example, a sequence adaptor can be a hairpin shaped adaptorand/or comprises a sample barcode. Further, the resulting fragments canbe amplified and sequenced. In some cases, the adaptor does not comprisea sequencing primer region.

The method can include sequencing one or both strands of the DNAfragments. In one case, both strands of the DNA fragment areindependently sequenced. The tagged, amplified, and/or selectivelyenriched DNA fragments are sequenced to obtain sequence reads thatcomprise sequence information of the molecular barcodes and at least aportion of the original DNA fragments.

The method can include reducing or tracking redundancy (as describedabove) in the sequence reads to determine consensus reads that arerepresentative of single-strands of the original DNA fragments. Forexample, to reduce or track redundancy, the method can include comparingsequence reads having the same or similar molecular barcodes and thesame or similar end of fragment sequences. The method can compriseperforming a phylogentic analysis on the sequence reads having the sameor similar molecular barcodes. The molecular barcodes can have a barcodewith varying edit distances (including any edit distances as describedthroughout), for example, an edit distance of up to 3. The end of thefragment sequences can include fragment sequences having an editdistance with varying distances (including any edit distances asdescribed throughout), for example, an edit distance of up to 3.

The method can comprise binning the sequence reads according to themolecular barcodes and sequence information. For example, binning thesequence reads according to the molecular barcodes and sequenceinformation can be performed from at least one end of each of theoriginal DNA fragments to create bins of single stranded reads. Themethod can further comprise in each bin, determining a sequence of agiven original DNA fragment among the original DNA fragments byanalyzing sequence reads.

In some cases, sequence reads in each bin can be collapsed to aconsensus sequence and subsequently mapped to a genome. As analternative, sequence reads can be mapped to a genome prior to binningand subsequently collapsed to a consensus sequence.

The method can also comprise sorting sequence reads into paired readsand unpaired reads. After sorting, the number of paired reads andunpaired reads that map to each of one or more genetic loci can bequantified.

The method can include quantifying the consensus reads to detect and/orquantify the rare DNA, which are described throughout. The method cancomprise detecting and/or quantifying the rare DNA by comparing a numberof times each base occurs at each position of a genome represented bythe tagged, amplified, and/or enriched DNA fragments.

The method can comprise tagging the original DNA fragments in a singlereaction using a library of tags. The library can include at least 2, atleast 3, at least 4, at least 5, at least 6, at least 7, at least 8, atleast 9, at least 10, at least 11, at least 12, at least 13, at least14, at least 15, at least 16, at least 17, at least 18, at least 19, atleast 20, at least 50, at least 100, at least 500, at least 1000, atleast 5000, at least 10000, or any number of tags as disclosedthroughout. For example, the library of tags can include at least 8tags. The library of tags can include 8 tags (which can generate 64different possible combinations). The method can be conducted such thata high percentage of fragments, e.g., greater than 50% (or anypercentages as described throughout) are tagged at both ends, whereineach of the tags comprises a molecular barcode.

M. Processing and/or Analyzing Nucleic Acids

The methods described throughout can be used for processing and/oranalyzing a nucleic acid sample of a subject. The method can comprisingexposing polynucleotide fragments of the nucleic acid sample to aplurality of polynucleotide molecules to yield tagged polynucleotidefragments. The plurality of polynucleotide molecules that can be usedare described throughout the application.

For example, the plurality of polynucleotide molecules can be each lessthan or equal to 40 nucleic acid bases in length and have distinctbarcode sequences with respect to at least 4 nucleic acid bases and anedit distance of at least 1, wherein each of the distinct barcodesequences is within 20 nucleic acid bases from a terminal end of arespective one of the plurality of polynucleotide molecules, and whereinthe plurality of polynucleotide molecules are not sequencing adaptors.

The tagged polynucleotide fragments can be subjected to nucleic acidamplification reactions under conditions that yield amplifiedpolynucleotide fragments as amplification products of the taggedpolynucleotide fragments. After amplification, the nucleotide sequenceof the amplified tagged polynucleotide fragments is determined. In somecases, the nucleotide sequences of the amplified tagged polynucleotidefragments are determined without the use of polymerase chain reaction(PCR).

The method can comprise analyzing the nucleotide sequences with aprogrammed computer processor to identify one or more genetic variantsin the nucleotide sample of the subject. Any genetic alterations can beidentified, including but not limited to, base change(s), insertion(s),repeat(s), deletion(s), copy number variation(s), epigeneticmodification(s), nucleosome binding site(s), copy number change(s) dueto origin(s) of replication, and transversion(s). Other geneticalterations can include, but are not limited to, one or more tumorassociated genetic alterations.

The subject of the methods can be suspected of having a disease. Forexample, the subject can be suspected of having cancer. The method cancomprise collecting a nucleic acid sample from a subject. The nucleicacid sample can be collected from blood, plasma, serum, urine, saliva,mucosal excretions, sputum, stool, cerebral spinal fluid, skin, hair,sweat, and/or tears. The nucleic acid sample can be a cell-free nucleicacid sample. In some cases, the nucleic acid sample is collected from nomore than 100 nanograms (ng) of double-stranded polynucleotide moleculesof the subject.

The polynucleotide fragments can comprise double-stranded polynucleotidemolecules. In some cases, the plurality of polynucleotide molecules arecoupled to the polynucleotide fragments via blunt end ligation, stickyend ligation, molecular inversion probes, polymerase chain reaction(PCR), ligation-based PCR, multiplex PCR, single strand ligation, orsingle strand circularization.

The method as described herein results in high efficiency tagging ofnucleic acids. For example, exposing the polynucleotide fragments of thenucleic acid sample to the plurality of polynucleotide molecules yieldsthe tagged polynucleotide fragments with a conversion efficiency of atleast 30%, e.g., of at least 50% (e.g., 60%, 70%, 80%, 90%, 95%, or99%). Conversion efficiency of at least 30%, 35%, 40%, 45%, 50%, 51%,52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%,66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%,80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%,94%, 95%, 96%, 97%, 98%, or 99% can be achieved.

The method can result in a tagged polynucleotide fragment that sharecommon polynucleotide molecules. For example, any of at least 5%, 6%,7%, 8%, 9%, 10%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%,75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% or 100% of the taggedpolynucleotide fragments share a common polynucleotide molecule. Themethod can comprise generating the polynucleotide fragments from thenucleic acid sample.

In some cases, the subjecting of the method comprises amplifying thetagged polynucleotide fragments in the presence primers corresponding toa plurality of genes selected from the group consisting of ALK, APC,BRAF, CDKN2A, EGFR, ERBB2, FBXW7, KRAS, MYC, NOTCH1, NRAS, PIK3CA, PTEN,RB1, TP53, MET, AR, ABL1, AKT1, ATM, CDH1, CSF1R, CTNNB1, ERBB4, EZH2,FGFR1, FGFR2, FGFR3, FLT3, GNA11, GNAQ, GNAS, HNF1A, HRAS, IDH1, IDH2,JAK2, JAK3, KDR, KIT, MLH1, MPL, NPM1, PDGFRA, PROC, PTPN11, RET, SMAD4,SMARCB1, SMO, SRC, STK11, VHL, TERT, CCND1, CDK4, CDKN2B, RAF1, BRCA1,CCND2, CDK6, NF1, TP53, ARID1A, BRCA2, CCNE1, ESR1, RIT1, GATA3, MAP2K1,RHEB, ROS1, ARAF, MAP2K2, NFE2L2, RHOA, and NTRK1. Additionally, anycombination of these genes can be amplified. For example, 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, or all 54 of these genes canbe amplified.

The methods described herein can comprise generating a plurality ofsequence reads from a plurality of polynucleotide molecules. Theplurality of polynucleotide molecules can cover genomic loci of a targetgenome. For example, the genomic loci can correspond to a plurality ofgenes as listed above. Further, the genomic loci can be any combinationof these genes. Any given genomic locus can comprise at least twonucleic acid bases. Any given genomic locus can also comprise aplurality of nucleic acid bases, for example, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,46, 47, 48, 49, 50, or more nucleic acid bases.

The method can comprise grouping with a computer processor the pluralityof sequence reads into families. Each of the family can comprisessequence reads from one of the template polynucleotides. Each family cancomprise sequence reads from only one of the template polynucleotides.For each of the family, the sequence reads can be merged to generate aconsensus sequence. The grouping can comprise classifying the pluralityof sequence reads into families by identifying (i) distinct molecularbarcodes coupled to the plurality of polynucleotide molecules and (ii)similarities between the plurality of sequence reads, wherein eachfamily includes a plurality of nucleic acid sequences that areassociated with a distinct combination of molecular barcodes and similaror identical sequence reads.

Once merged, a consensus sequence can be called at a given genomic locusamong the genomic loci. At any given genomic loci, any of the followingcan be determined: i) genetic variants among the calls; ii) frequency ofa genetic alteration among the calls; iii) total number of calls; andiv) total number of alterations among the calls. The calling cancomprise calling at least one nucleic acid base at the given genomiclocus. The calling can also comprise calling a plurality of nucleic acidbases at the given genomic locus. In some cases, the calling cancomprise phylogenetic analysis, voting (e.g., biased voting), weighing,assigning a probability to each read at the locus in a family, orcalling the base with the highest probability. The consensus sequencecan be generated by evaluating a quantitative measure or a statisticalsignificance level for each of the sequence reads. If a quantitativemeasure is performed, the method can comprise use of a binomialdistribution, exponential distribution, beta distribution, or empiricaldistribution. However, frequency of the base at the particular locationcan also be used for calling, for example, if 51% or more of the readsis a “A” at the location, then the base may be called an “A” at thatparticular location. The method can further comprise mapping a consensussequence to a target genome.

The method can further comprising performing consensus calling at anadditional genomic locus among the genomic loci. The method can comprisedetermining a variation in copy number at one of the given genomic locusand additional genomic locus based on counts at the given genomic locusand additional genomic locus.

The methods described herein can comprise providing templatepolynucleotide molecules and a library of adaptor polynucleotidemolecules in a reaction vessel. The adaptor polynucleotide molecules canhave from 2 to 1,000 different barcode sequences and in some cases arenot sequencing adaptors. Other variations of adaptor polynucleotidemolecules are described throughout, which can also be used in themethods.

The polynucleotide molecules of the adaptors can have the same sampletag. The adaptor polynucleotide molecules can be coupled to both ends ofthe template polynucleotide molecules. The method can comprise couplingthe adaptor polynucleotide molecules to the template polynucleotidemolecules at an efficiency of at least 30%, e.g., of at least 50% (e.g.,60%, 70%, 80%, 90%, 95%, or 99%), thereby tagging each templatepolynucleotide with a tagging combination that is among 4 to 1,000,000different tagging combinations, to produce tagged polynucleotidemolecules. In some cases, the reaction can occur in a single reactionvessel. Coupling efficiency can also be at least 30%, 35%, 40%, 45%,50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%,64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%,78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. Tagging can be non-uniquetagging.

The tagged polynucleotide molecules can then be subject to anamplification reaction under conditions that will yield amplifiedpolynucleotide molecules as amplification products of the taggedpolynucleotide molecules. The template polynucleotide molecules can bedouble-stranded. Further, the template polynucleotide molecules can beblunt ended. In some cases, the amplification reaction comprisesnon-specifically amplifying the tagged polynucleotide molecules. Theamplification reaction can also comprises using a priming site toamplify each of the tagged polynucleotide molecules. The priming sitecan be a primer, e.g., a universal primer. The priming site can also bea nick.

The method can also comprise sequencing the amplified polynucleotidemolecules. The sequencing can comprise (i) subjecting the amplifiedpolynucleotide molecules to an additional amplification reaction underconditions that yield additional amplified polynucleotide molecules asamplification products of the amplified polynucleotide molecules, and/or(ii) sequencing the additional amplified polynucleotide molecules. Theadditional amplification can be performed in the presence of primerscomprising flow cells sequences, which will produce polynucleotidemolecules that are capable of binding to a flow cell. The additionalamplification can also be performed in the presence of primerscomprising sequences for hairpin shaped adaptors. The hairpin shapedadaptors can be attached to both ends of a polynucleotide fragment togenerate a circular molecule, which can be sequenced multiple times. Themethod can further comprise identifying genetic variants upon sequencingthe amplified polynucleotide molecules.

The method can further comprising separating polynucleotide moleculescomprising one or more given sequences from the amplified polynucleotidemolecules, to produce enriched polynucleotide molecules. The method canalso comprise amplifying the enriched polynucleotide molecules withprimers comprising the flow cell sequences. This amplification withprimers comprising flow cell sequences will produce polynucleotidemolecules that are capable of binding to a flow cell. The amplificationcan also be performed in the presence of primers comprising sequencesfor hairpin shaped adaptors. The hairpin shaped adaptors can be attachedto both ends of a polynucleotide fragment to generate a circularmolecule, which can be sequenced multiple times.

Flow cell sequences or hairpin shaped adaptors can be added bynon-amplification methods such as through ligation of such sequences.Other techniques such as hybridization methods can be used, e.g.,nucleotide overhangs.

The method can be performed without aliquoting the tagged polynucleotidemolecules. For example, once the tagged polynucleotide molecule is made,the amplification and sequencing can occur in the same tube without anyfurther preparation.

The methods described herein can be useful in detecting singlenucleotide variations (SNV), copy number variations (CNV), insertions,deletions, and/or rearrangements. In some cases, the SNVs, CNVs,insertions, deletions, and/or rearrangements, can be associated withdisease, for example, cancer.

N. Monitoring a Patient's Status

Methods disclosed herein can also be used to monitor a patient's diseasestatus. The disease of a subject can be monitored over time to determinea progression of the disease (e.g., regression). Markers indicative ofthe disease can be monitored in a biological sample of the subject, suchas a cell-free DNA sample.

For example, monitoring a subject's cancer status can comprise (a)determining an amount of one or more SNVs or copy numbers of a pluralityof genes (e.g., in an exon), (b) repeating such determination atdifferent points in time, and (c) determining if there is a differencein the number of SNVs, level of SNVs, number or level of genomicrearrangements, or copy numbers between (a) and (b). The genes can beselected from the group consisting of ALK, APC, BRAF, CDKN2A, EGFR,ERBB2, FBXW7, KRAS, MYC, NOTCH1, NRAS, PIK3CA, PTEN, RB1, TP53, MET, AR,ABL1, AKT1, ATM, CDH1, CSF1R, CTNNB1, ERBB4, EZH2, FGFR1, FGFR2, FGFR3,FLT3, GNA11, GNAQ, GNAS, HNF1A, HRAS, IDH1, IDH2, JAK2, JAK3, KDR, KIT,MLH1, MPL, NPM1, PDGFRA, PROC, PTPN11, RET, SMAD4, SMARCB1, SMO, SRC,STK11, VHL, TERT, CCND1, CDK4, CDKN2B, RAF1, BRCA1, CCND2, CDK6, NF1,TP53, ARID1A, BRCA2, CCNE1, ESR1, RIT1, GATA3, MAP2K1, RHEB, ROS1, ARAF,MAP2K2, NFE2L2, RHOA, and NTRK1. The genes can be selected from any 5,10, 15, 20, 30, 40, 50, or all of the genes in this group.

O. Sensitivity and Specificity

Methods disclosed herein can be used to detect cancer polynucleotides ina sample, and cancer in a subject, with high measures of agreement,e.g., high sensitivity and/or specificity. For example, such methods candetect cancer polynucleotides (e.g., rare DNA) in a sample at aconcentration that is less than 5%, 1%, 0.5%, 0.1%, 0.05%, or 0.01%, ata specificity of at least 99%, 99.9%, 99.99%, 99.999%, 99.9999%, or99.99999%. Such polynucleotides may be indicative of cancer or otherdisease. Further, such methods can detect cancer polynucleotides in asample with a positive predictive value of at least 90%, 91%, 92%, 93%,94%, 95%, 96%, 97%, 98%, 99%, 99.9%, 99.99%, 99.999%, or 99.9999%.

Subjects identified as positive in a test that are in reality positiveare referred as true positives (TP). Subjects identified as positive ina test that are in reality negative are referred as false positives(FP). Subjects identified as negative in a test that are in realitynegative are referred as true negatives (TN). Subjects identified asnegative in a test that are in reality positive are referred as falsenegatives (FN). Sensitivity is the percentage of actual positivesidentified in a test as positive. This includes, for example, instancesin which one should have found a cancer genetic variant and did.(Sensitivity=TP/(TP+FN).) Specificity is the percentage of actualnegatives identified in a test as negative. This includes, for example,instances in which one should have found no cancer genetic variant anddid not. Specificity can be calculated using the following equation:Specificity=TN/(TN+FP). Positive predictive value (PPV) can be measuredby the percentage of subjects who test positive that are true positives.PPV can be calculated using the following equation: PPV=TP/(TP+FP).Positive predictive value can be increased by increasing sensitivity(e.g., chance of an actual positive being detected) and/or specificity(e.g., chance of not mistaking an actual negative for a positive).

Low conversion rates of polynucleotides into adaptor-taggedpolynucleotides can compromise sensitivity as it decreases the chance ofconverting, and therefore detecting, rare polynucleotide targets. Noisein a test can compromise specificity as it increases the number of falsepositives detected in a test. Both low conversion rate and noisecompromise positive predictive value as they decrease the percentage oftrue positives and increase the percentage of false positives.

The methods disclosed herein can achieve high levels of agreement, e.g.,sensitivity and specificity, leading to high positive predictive values.Methods of increasing sensitivity include high efficiency conversion ofpolynucleotides into adaptor-tagged polynucleotides in a sample. Methodsof increasing specificity include reducing sequencing errors, forexample, by molecular tracking.

Methods of the present disclosure can be used to detect geneticvariation in non-uniquely tagged initial starting genetic material(e.g., rare DNA) at a concentration that is less than 5%, 1%, 0.5%,0.1%, 0.05%, or 0.01%, at a specificity of at least 99%, 99.9%, 99.99%,99.999%, 99.9999%, or 99.99999%. In some aspects, the methods canfurther comprise converting polynucleotides in the initial startingmaterial at an efficiency of at least at least 10%, at least 20%, atleast 30%, at least 40%, at least 50%, at least 60%, at least 70%, atleast 80%, or at least 90%. Sequence reads of tagged polynucleotides canbe subsequently tracked to generate consensus sequences forpolynucleotides with an error rate of no more than 2%, 1%, 0.1%, or0.01%.

2. Pooling Methods

Disclosed herein are methods of detecting copy number variation and/orsequence variants at one or more genetic loci in a test sample. Oneembodiment is shown in FIG. 8. Typically, detecting copy numbervariation involves determining a quantitative measure (e.g., an absoluteor relative number) of polynucleotides mapping to a genetic locus ofinterest in a genome of a test sample, and comparing that number to aquantitative measure of polynucleotides mapping to that locus in acontrol sample. In certain methods, the quantitative measure isdetermined by comparing the number of molecules in the test sample thatmap to a locus of interest with a number of molecules in the test samplemapping to a reference sequence, e.g., a sequence expected to be presentat wild type ploidy number. In some examples, the reference sequence isHG19, build 37, or build 38. The comparison could involve, for example,determining a ratio. Then, this measure is compared with a similarmeasure determined in a control sample. So, for example, if a testsample has a ratio of 1.5:1 for locus of interest versus referencelocus, and a control sample has a ratio of 1:1 for the same loci, onemay conclude that the test sample exhibits polyploidy at the locus ofinterest.

When the test sample and the control sample are analyzed separately, thework flow can introduce distortions between final numbers in the controland test samples.

In one method disclosed herein (e.g., flow chart 800), polynucleotidesare provided from a test and a control sample (802). Polynucleotides ina test sample and those in a control sample are tagged with tags thatidentify the polynucleotides as originating from the test or controlsample (a source tag). (804.) The tag can be, for example, apolynucleotide sequence or barcode that unambiguously identifies thesource.

The polynucleotides in each of the control and test samples also can betagged with identifier tags that will be carried by all amplificationprogeny of a polynucleotide. Information from start and end sequences ofa polynucleotide and identifier tags can identify sequence reads frompolynucleotides amplified from an original parent molecule. Eachmolecule can be uniquely tagged compared with other molecules in thesample. Alternatively, each molecule need not be uniquely taggedcompared with other molecules in the sample. That is, the number ofdifferent identifier sequences can be fewer than that the number ofmolecules in sample. By combining identifier information with start/stopsequence information, the probability of confusing two molecules havingthe same start/stop sequence is significantly diminished.

Number of different identifiers used to tag a nucleic acid (e.g., cfDNA)can dependent on the number of different haploid genome equivalents.Different identifiers can be used to tag at least 2, least 10, least100, least 200, least 300, least 400, least 500, least 600, least 700,least 800, least 900, least 1,000, least 2,000, least 3,000, least4,000, least 5,000, least 6,000, least 7,000, least 8,000, least 9,000,least 10,000 or more different haploid genome equivalents. Accordingly,the number of different identifiers used to tag a nucleic acid sample,e.g., cell-free DNA from 500 to 10,000 different haploid genomeequivalents and be between any of 1, 2, 3, 4 and 5 and no more than 100,90, 80, 70, 60, 50, 40 or 30. For example, the number of differentidentifier used to tag a nucleic acid sample from 500 to 10,000different haploid genome equivalents can be 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82,83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100or less.

Polynucleotides can be tagged by ligation of adaptors comprising thetags or identifiers before amplification. Ligation can be performedusing an enzyme, e.g., a ligase. For example, tagging can be performedusing a DNA ligase. The DNA ligase can be a T4 DNA ligase, E. coli DNAligase, and/or mammalian ligase. The mammalian ligase can be DNA ligaseI, DNA ligase III, or DNA ligase IV. The ligase may also be athermostable ligase. Tags can be ligated to a blunt-end of apolynucleotide (blunt-end ligation). Alternatively, tags can be ligatedto a sticky end of a polynucleotide (sticky-end ligation). Thepolynucleotides can be tagged by blunt end ligation using adaptors(e.g., adaptors having forked ends). High efficiency of ligation can beachieved using high excess of adaptors (e.g., more than 1.5×, more than2×, more than 3×, more than 4×, more than 5×, more than 6×, more than7×, more than 8×, more than 9×, more than 10×, more than 11×, more than12×, more than 13×, more than 14×, more than 15×, more than 20×, morethan 25×, more than 30×, more than 35×, more than 40×, more than 45×,more than 50×, more than 55×, more than 60×, more than 65×, more than70×, more than 75×, more than 80×, more than 85×, more than 90×, morethan 95×, or more than 100).

Once tagged with tags that identify source of the polynucleotides,polynucleotides from different sources (e.g., different samples) can bepooled. After pooling, polynucleotides from different sources (e.g.,different samples) can be distinguished by any measurement using thetags, including any process of quantitative measurement. For example, asshown in (806) (FIG. 8), polynucleotides from the control sample and thetest sample can be pooled. The pooled molecules can be subject to thesequencing (808) and bioinformatic work flow. Both will be subject tothe same variations in the process and, therefore, any differential biasis reduced. Because molecules originating from control and test samplesare differently tagged, they can be distinguished in any process ofquantitative measurement.

The relative amount of control and test sample pooled can be varied. Theamount of control sample can be same as the amount of test sample. Theamount of control sample can also be larger than the amount of testsample. Alternatively, the amount of control sample can be smaller thanthe amount of test sample. The smaller the relative amount of one sampleto the total, the fewer identifying tags needed in the original taggingprocess. A number can be selected to reduce to acceptable levels theprobability that two parent molecules having the same start/endsequences will bear the same identifying tag. This probability can beless than 10%, less than 1%, less than 0.1% or less than 0.01%. Theprobability can be less than 25%, 24%, 23%, 22%, 21%, 20%, 19%, 18%,17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%,or 1%.

Methods disclosed herein can also comprise grouping sequence reads. Forexample, bioinformatic workflow can include grouping sequence readsproduced from progeny of a single parent molecule, as shown in (810)(FIG. 8). This can involve any of the redundancy reduction methodsdescribed herein. Molecules sourced from test and control samples can bedifferentiated based on source tags they carry (812). Molecules mappingto a target locus are quantified for both test-sourced andcontrol-sourced molecules (812). This can include the normalizationmethods discussed herein, e.g., in which numbers at a target locus arenormalized against numbers at a reference locus.

Normalized (or raw) quantities at a target locus from test and controlsamples are compared to determine presence of copy number variation(814).

3. Computer Control Systems

The present disclosure provides computer control systems that areprogrammed to implement methods of the disclosure. FIG. 6 shows acomputer system 1501 that is programmed or otherwise configured toimplement the methods of the present disclosure. The computer system1501 can regulate various aspects sample preparation, sequencing and/oranalysis. In some examples, the computer system 1501 is configured toperform sample preparation and sample analysis, including nucleic acidsequencing. The computer system 1501 can be an electronic device of auser or a computer system that is remotely located with respect to theelectronic device. The electronic device can be a mobile electronicdevice.

The computer system 1501 includes a central processing unit (CPU, also“processor” and “computer processor” herein) 1505, which can be a singlecore or multi core processor, or a plurality of processors for parallelprocessing. The computer system 1501 also includes memory or memorylocation 1510 (e.g., random-access memory, read-only memory, flashmemory), electronic storage unit 1515 (e.g., hard disk), communicationinterface 1520 (e.g., network adapter) for communicating with one ormore other systems, and peripheral devices 1525, such as cache, othermemory, data storage and/or electronic display adapters. The memory1510, storage unit 1515, interface 1520 and peripheral devices 1525 arein communication with the CPU 1505 through a communication bus (solidlines), such as a motherboard. The storage unit 1515 can be a datastorage unit (or data repository) for storing data. The computer system1501 can be operatively coupled to a computer network (“network”) 1530with the aid of the communication interface 1520. The network 1530 canbe the Internet, an internet and/or extranet, or an intranet and/orextranet that is in communication with the Internet. The network 1530 insome cases is a telecommunication and/or data network. The network 1530can include one or more computer servers, which can enable distributedcomputing, such as cloud computing. The network 1530, in some cases withthe aid of the computer system 1501, can implement a peer-to-peernetwork, which may enable devices coupled to the computer system 1501 tobehave as a client or a server.

The CPU 1505 can execute a sequence of machine-readable instructions,which can be embodied in a program or software. The instructions may bestored in a memory location, such as the memory 1510. The instructionscan be directed to the CPU 1505, which can subsequently program orotherwise configure the CPU 1505 to implement methods of the presentdisclosure. Examples of operations performed by the CPU 1505 can includefetch, decode, execute, and writeback.

The CPU 1505 can be part of a circuit, such as an integrated circuit.One or more other components of the system 1501 can be included in thecircuit. In some cases, the circuit is an application specificintegrated circuit (ASIC).

The storage unit 1515 can store files, such as drivers, libraries andsaved programs. The storage unit 1515 can store user data, e.g., userpreferences and user programs. The computer system 1501 in some casescan include one or more additional data storage units that are externalto the computer system 1501, such as located on a remote server that isin communication with the computer system 1501 through an intranet orthe Internet.

The computer system 1501 can communicate with one or more remotecomputer systems through the network 1530. For instance, the computersystem 1501 can communicate with a remote computer system of a user(e.g., an operator). Examples of remote computer systems includepersonal computers (e.g., portable PC), slate or tablet PC's (e.g.,Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g.,Apple® iPhone, Android-enabled device, Blackberry®), or personal digitalassistants. The user can access the computer system 1501 via the network1530.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the computer system 1501, such as, for example, on thememory 1510 or electronic storage unit 1515. The machine executable ormachine readable code can be provided in the form of software. Duringuse, the code can be executed by the processor 1505. In some cases, thecode can be retrieved from the storage unit 1515 and stored on thememory 1510 for ready access by the processor 1505. In some situations,the electronic storage unit 1515 can be precluded, andmachine-executable instructions are stored on memory 1510.

The code can be pre-compiled and configured for use with a machine havea processer adapted to execute the code, or can be compiled duringruntime. The code can be supplied in a programming language that can beselected to enable the code to execute in a pre-compiled or as-compiledfashion.

Aspects of the systems and methods provided herein, such as the computersystem 1501, can be embodied in programming. Various aspects of thetechnology may be thought of as “products” or “articles of manufacture”typically in the form of machine (or processor) executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Machine-executable code can be stored on an electronicstorage unit, such memory (e.g., read-only memory, random-access memory,flash memory) or a hard disk. “Storage” type media can include any orall of the tangible memory of the computers, processors or the like, orassociated modules thereof, such as various semiconductor memories, tapedrives, disk drives and the like, which may provide non-transitorystorage at any time for the software programming. All or portions of thesoftware may at times be communicated through the Internet or variousother telecommunication networks. Such communications, for example, mayenable loading of the software from one computer or processor intoanother, for example, from a management server or host computer into thecomputer platform of an application server. Thus, another type of mediathat may bear the software elements includes optical, electrical andelectromagnetic waves, such as used across physical interfaces betweenlocal devices, through wired and optical landline networks and overvarious air-links. The physical elements that carry such waves, such aswired or wireless links, optical links or the like, also may beconsidered as media bearing the software. As used herein, unlessrestricted to non-transitory, tangible “storage” media, terms such ascomputer or machine “readable medium” refer to any medium thatparticipates in providing instructions to a processor for execution.

Hence, a machine readable medium, such as computer-executable code, maytake many forms, including but not limited to, a tangible storagemedium, a carrier wave medium or physical transmission medium.Non-volatile storage media include, for example, optical or magneticdisks, such as any of the storage devices in any computer(s) or thelike, such as may be used to implement the databases, etc. shown in thedrawings. Volatile storage media include dynamic memory, such as mainmemory of such a computer platform. Tangible transmission media includecoaxial cables; copper wire and fiber optics, including the wires thatcomprise a bus within a computer system. Carrier-wave transmission mediamay take the form of electric or electromagnetic signals, or acoustic orlight waves such as those generated during radio frequency (RF) andinfrared (IR) data communications. Common forms of computer-readablemedia therefore include for example: a floppy disk, a flexible disk,hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD orDVD-ROM, any other optical medium, punch cards paper tape, any otherphysical storage medium with patterns of holes, a RAM, a ROM, a PROM andEPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

The computer system 1501 can include or be in communication with anelectronic display 1535 that comprises a user interface (UI) 1540. TheUI can allow a user to set various conditions for the methods describedherein, for example, PCR or sequencing conditions. Examples of UI'sinclude, without limitation, a graphical user interface (GUI) andweb-based user interface.

Methods and systems of the present disclosure can be implemented by wayof one or more algorithms. An algorithm can be implemented by way ofsoftware upon execution by the central processing unit 1505. Thealgorithm can, for example, process the reads to generate a consequencesequence.

FIG. 7 schematically illustrates another system for analyzing a samplecomprising nucleic acids from a subject. The system includes asequencer, bioinformatic software and internet connection for reportanalysis by, for example, a hand held device or a desktop computer

Disclosed herein is a system for analyzing a target nucleic acidmolecule of a subject, comprising: a communication interface thatreceives nucleic acid sequence reads for a plurality of polynucleotidemolecules that cover genomic loci of a target genome; computer memorythat stores the nucleic acid sequence reads for the plurality ofpolynucleotide molecules received by the communication interface; and acomputer processor operatively coupled to the communication interfaceand the memory and programmed to (i) group the plurality of sequencereads into families, wherein each family comprises sequence reads fromone of the template polynucleotides, (ii) for each of the families,merge sequence reads to generate a consensus sequence, (iii) call theconsensus sequence at a given genomic locus among the genomic loci, and(iv) detect at the given genomic locus any of genetic variants among thecalls, frequency of a genetic alteration among the calls, total numberof calls; and total number of alterations among the calls, wherein thegenomic loci correspond to a plurality of genes selected from the groupconsisting of ALK, APC, BRAF, CDKN2A, EGFR, ERBB2, FBXW7, KRAS, MYC,NOTCH1, NRAS, PIK3CA, PTEN, RB1, TP53, MET, AR, ABL1, AKT1, ATM, CDH1,CSF1R, CTNNB1, ERBB4, EZH2, FGFR1, FGFR2, FGFR3, FLT3, GNA11, GNAQ,GNAS, HNF1A, HRAS, IDH1, IDH2, JAK2, JAK3, KDR, KIT, MLH1, MPL, NPM1,PDGFRA, PROC, PTPN11, RET, SMAD4, SMARCB1, SMO, SRC, STK11, VHL, TERT,CCND1, CDK4, CDKN2B, RAF1, BRCA1, CCND2, CDK6, NF1, TP53, ARID1A, BRCA2,CCNE1, ESR1, RIT1, GATA3, MAP2K1, RHEB, ROS1, ARAF, MAP2K2, NFE2L2,RHOA, and NTRK1. The different variations of each component of thesystem are described throughout the disclosure within the methods andcompositions. These individual components and variations thereof, arealso applicable in this system.

4. Kits

Kits comprising the compositions as described herein. The kits can beuseful in performing the methods as described herein. Disclosed hereinis a kit comprising a plurality of oligonucleotide probes thatselectively hybridize to least 5, 6, 7, 8, 9, 10, 20, 30, 40 or allgenes selected from the group consisting of ALK, APC, BRAF, CDKN2A,EGFR, ERBB2, FBXW7, KRAS, MYC, NOTCH1, NRAS, PIK3CA, PTEN, RB1, TP53,MET, AR, ABL1, AKT1, ATM, CDH1, CSF1R, CTNNB1, ERBB4, EZH2, FGFR1,FGFR2, FGFR3, FLT3, GNA11, GNAQ, GNAS, HNF1A, HRAS, IDH1, IDH2, JAK2,JAK3, KDR, KIT, MLH1, MPL, NPM1, PDGFRA, PROC, PTPN11, RET, SMAD4,SMARCB1, SMO, SRC, STK11, VHL, TERT, CCND1, CDK4, CDKN2B, RAF1, BRCA1,CCND2, CDK6, NF1, TP53, ARID1A, BRCA2, CCNE1, ESR1, RIT1, GATA3, MAP2K1,RHEB, ROS1, ARAF, MAP2K2, NFE2L2, RHOA, and NTRK1. The number genes towhich the oligonucleotide probes can selectively hybridize can vary. Forexample, the number of genes can comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,47, 48, 49, 50, 51, 52, 53, or 54. The kit can include a container thatincludes the plurality of oligonucleotide probes and instructions forperforming any of the methods described herein.

The oligonucleotide probes can selectively hybridize to exon regions ofthe genes, e.g., of the at least 5 genes. In some cases, theoligonucleotide probes can selectively hybridize to at least 30 exons ofthe genes, e.g., of the at least 5 genes. In some cases, the multipleprobes can selectively hybridize to each of the at least 30 exons. Theprobes that hybridize to each exon can have sequences that overlap withat least 1 other probe. In some embodiments, the oligoprobes canselectively hybridize to non-coding regions of genes disclosed herein,for example, intronic regions of the genes. The oligoprobes can alsoselectively hybridize to regions of genes comprising both exonic andintronic regions of the genes disclosed herein.

Any number of exons can be targeted by the oligonucleotide probes. Forexample, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70,75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145,150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215,220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285,290, 295, 300, 400, 500, 600, 700, 800, 900, 1,000, or more, exons canbe targeted.

The kit can comprise at least 4, 5, 6, 7, or 8 different libraryadaptors having distinct molecular barcodes and identical samplebarcodes. The library adaptors may not be sequencing adaptors. Forexample, the library adaptors do not include flow cell sequences orsequences that permit the formation of hairpin loops for sequencing. Thedifferent variations and combinations of molecular barcodes and samplebarcodes are described throughout, and are applicable to the kit.Further, in some cases, the adaptors are not sequencing adaptors.Additionally, the adaptors provided with the kit can also comprisesequencing adaptors. A sequencing adaptor can comprise a sequencehybridizing to one or more sequencing primers. A sequencing adaptor canfurther comprise a sequence hybridizing to a solid support, e.g., a flowcell sequence. For example, a sequencing adaptor can be a flow celladaptor. The sequencing adaptors can be attached to one or both ends ofa polynucleotide fragment. In some cases, the kit can comprise at least8 different library adaptors having distinct molecular barcodes andidentical sample barcodes. The library adaptors may not be sequencingadaptors. The kit can further include a sequencing adaptor having afirst sequence that selectively hybridizes to the library adaptors and asecond sequence that selectively hybridizes to a flow cell sequence. Inanother example, a sequencing adaptor can be hairpin shaped. Forexample, the hairpin shaped adaptor can comprise a complementary doublestranded portion and a loop portion, where the double stranded portioncan be attached (e.g., ligated) to a double-stranded polynucleotide.Hairpin shaped sequencing adaptors can be attached to both ends of apolynucleotide fragment to generate a circular molecule, which can besequenced multiple times. A sequencing adaptor can be up to 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, or morebases from end to end. The sequencing adaptor can comprise 20-30, 20-40,30-50, 30-60, 40-60, 40-70, 50-60, 50-70, bases from end to end. In aparticular example, the sequencing adaptor can comprise 20-30 bases fromend to end. In another example, the sequencing adaptor can comprise50-60 bases from end to end. A sequencing adaptor can comprise one ormore barcodes. For example, a sequencing adaptor can comprise a samplebarcode. The sample barcode can comprise a pre-determined sequence. Thesample barcodes can be used to identify the source of thepolynucleotides. The sample barcode can be at least 1, 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, ormore (or any length as described throughout) nucleic acid bases, e.g.,at least 8 bases. The barcode can be contiguous or non-contiguoussequences, as described above.

The library adaptors can be blunt ended and Y-shaped and can be lessthan or equal to 40 nucleic acid bases in length. Other variations ofthe can be found throughout and are applicable to the kit.

EXAMPLES Example 1. Methods for Copy Number Variation Detection

Blood Collection

10-30 mL Blood samples are collected at room temperature. The samplesare centrifuged to remove cells. Plasma is collected aftercentrifugation.

cfDNA Extraction

The sample is subjected to proteinase K digestion. DNA is precipitatedwith isopropanol. DNA is captured on a DNA purification column (e.g., aQIAamp DNA Blood Mini Kit) and eluted in 100 μl solution. DNAs below 500bp are selected with Ampure SPRI magnetic bead capture (PEG/salt). Theresulting production is suspended in 30 μl H₂O. Size distribution ischecked (major peak=166 nucleotides; minor peak=330 nucleotides) andquantified. 5 ng of extracted DNA contain approximately 1700 haploidgenome equivalents (“HGE”). The general correlation between the amountof DNA and HGE is as follow: 3 pg DNA=1 HGE; 3 ng DNA=1K HGE; 3 μgDNA=1M HGE; 10 pg DNA=3 HE; 10 ng DNA=3K HGE; 10 μg DNA=3M HGE.

“Single Molecule” Library Prep

High-efficiency DNA tagging (>80%) is performed by blunt-end repair andligation with 8 different octomers (i.e., 64 combinations) withoverloaded hairpin adaptors. 2.5 ng DNA (i.e. approximately 800 HGE) isused as the starting material. Each hairpin adaptor comprises a randomsequence on its non-complementary portion. Both ends of each DNAfragment are attached with hairpin adaptors. Each tagged fragment can beidentified by the random sequence on the hairpin adaptors and a 10 pendogenous sequence on the fragment.

Tagged DNA is amplified by 10 cycles of PCR to produce about 1-7 μg DNAsthat contain approximately 500 copies of each of the 800 HGE in thestarting material.

Buffer optimization, polymerase optimization and cycle reduction may beperformed to optimize the PCR reactions. Amplification bias, e.g.,non-specific bias, GC bias, and/or size bias are also reduced byoptimization. Noise(s) (e.g., polymerase-introduced errors) are reducedby using high-fidelity polymerases.

The Library may be prepared using Verniata or Sequenom methods.

Sequences may be enriched as follow: DNAs with regions of interest (ROI)are captured using biotin-labeled bead with probe to ROIs. The ROIs areamplified with 12 cycles of PCR to generate a 2000 times amplification.The resulting DNA is then denatured and diluted to 8 pM and loaded intoan Illumina sequencer.

Massively Parallel Sequencing

0.1 to 1% of the sample (approximately 100 pg) are used for sequencing.

Digital Bioinformatics

Sequence reads are grouped into families, with about 10 sequence readsin each family. Families are collapsed into consensus sequences byvoting (e.g., biased voting) each position in a family. A base is calledfor consensus sequence if 8 or 9 members agree. A base is not called forconsensus sequence if no more than 60% of the members agree.

The resulting consensus sequences are mapped to a reference genome. Eachbase in a consensus sequence is covered by about 3000 differentfamilies. A quality score for each sequence is calculated and sequencesare filtered based on the their quality scores.

Sequence variation is detected by counting distribution of bases at eachlocus. If 98% of the reads have the same base (homozygous) and 2% have adifferent base, the locus is likely to have a sequence variant,presumably from cancer DNA.

CNV is detected by counting the total number of sequences (bases)mapping to a locus and comparing with a control locus. To increase CNVdetection, CNV analysis is performed specific regions, including regionson ALK, APC, BRAF, CDKN2A, EGFR, ERBB2, FBXW7, KRAS, MYC, NOTCH1, NRAS,PIK3CA, PTEN, RB1, TP53, MET, AR, ABL1, AKT1, ATM, CDH1, CSF1R, CTNNB1,ERBB4, EZH2, FGFR1, FGFR2, FGFR3, FLT3, GNA11, GNAQ, GNAS, HNF1A, HRAS,IDH1, IDH2, JAK2, JAK3, KDR, KIT, MLH1, MPL, NPM1, PDGFRA, PROC, PTPN11,RET, SMAD4, SMARCB1, SMO, SRC, STK11, VHL, TERT, CCND1, CDK4, CDKN2B,RAF1, BRCA1, CCND2, CDK6, NF1, TP53, ARID1A, BRCA2, CCNE1, ESR1, RIT1,GATA3, MAP2K1, RHEB, ROS1, ARAF, MAP2K2, NFE2L2, RHOA, or NTRK1 genes.

Example 2. Method for Correcting Base Calling by Determining the TotalNumber Unseen Molecules in a Sample

After fragments are amplified and the sequences of amplified fragmentsare read and aligned, the fragments are subjected to base calling.Variations in the number of amplified fragments and unseen amplifiedfragments can introduce errors in base calling. These variations arecorrected by calculating the number of unseen amplified fragments.

When base calling for locus A (an arbitrary locus), it is first assumedthat there are N amplified fragments. The sequence readouts can comefrom two types of fragments: double-strand fragments and single-strandfragments. The following is a theoretical example of calculating thetotal number of unseen molecules in a sample.

N is the total number of molecules in the sample.

Assuming 1000 is the number of duplexes detected.

Assuming 500 is the number of single-stranded molecule detected.

P is the probability of seeing a strand.

Q is the probability of not detecting a strand.

Since Q=1−P.

1000=NP(2).

500=N2PQ.

1000/P(2)=N.

500−2PQ=N.

1000/P(2)=500÷2PQ.

1000*2PQ=500P(2).

2000PQ=500P(2).

2000Q=500P.

2000(1−P)=500P

2000−2000P=500P.

2000=500P+2000P.

2000=2500P.

2000÷2500=P.

0.8=P.

1000/P(2)=N.

1000÷0.64=N.

1562=N.

Number of unseen fragments=62.

Example 3. Identification of Genetic Variants in Cancer-AssociatedSomatic Variants in a Patient

An assay is used to analyze a panel of genes to identify geneticvariants in cancer-associated somatic variants with high sensitivity.

Cell-free DNA is extracted from plasma of a patient and amplified byPCR. Genetic variants are analyzed by massively parallel sequencing ofthe amplified target genes. For one set of genes, all exons aresequenced as such sequencing coverage had shown to have clinicallyutility (Table 1). For another set of genes, sequencing coverageincluded those exons with a previously reported somatic mutation (Table2). The minimum detectable mutant allele (limit of detection) isdependent on the patient's sample cell-free DNA concentration, whichvaried from less than 10 to over 1,000 genomic equivalents per mL ofperipheral blood. Amplification may not be detected in samples withlower amounts of cell-free DNA and/or low-level gene copy amplification.Certain sample or variant characteristics resulted in reduced analyticsensitivity, such as low sample quality or improper collection.

The percentage of genetic variants found in cell-free DNA circulating inblood is related to the unique tumor biology of this patient. Factorsthat affected the amount/percentages of detected genetic variants incirculating cell-free DNA in blood include tumor growth, turn-over,size, heterogeneity, vascularization, disease progression or treatment.Table 3 annotates the percentage, or allele frequency, of alteredcirculating cell-free DNA (% cfDNA) detected in this patient. Some ofthe detected genetic variants are listed in descending order by % cfDNA.

Genetic variants are detected in the circulating cell-free DNA isolatedfrom this patient's blood specimen. These genetic variants arecancer-associated somatic variants, some of which have been associatedwith either increased or reduced clinical response to specifictreatment. “Minor Alterations” are defined as those alterations detectedat less than 10% the allele frequency of “Major Alterations”. Thedetected allele frequencies of these alterations (Table 3) andassociated treatments for this patient are annotated.

All genes listed in Tables 1 and 2 are analyzed as part of theGuardant360™ test. Amplification is not detected for ERBB2, EGFR, or METin the circulating cell-free DNA isolated from this patient's bloodspecimen.

Patient test results comprising the genetic variants are listed in Table4.

TABLE 1 Genes in which all exons are sequenced GENES IN WHICH ALL EXONSARE SEQUENCED ALK <0.1% APC <0.1% AR <0.1% BRAF <0.1% CDKN2A <0.1% EGFR<0.1% ERBB2 <0.1% FBXW7 <0.1% KRAS <0.1% MET <0.1% MYC <0.1% NOTCH1<0.1% NRAS <0.1% PIK3CA <0.1% PTEN <0.1% PROC <0.1% RB1 <0.1% TP53 <0.1%LOD: Limit of Detection. The minimum detectable mutant allele frequencyfor this specimen in which 80% of somatic variants is detected.

TABLE 2 Genes in which exons with a previously reported somatic mutationare sequenced GENES IN WHICH EXONS WITH A PREVIOUSLY REPORTED SOMATICMUTATION ARE SEQUENCED ABL1 <0.1% AKT1 <0.1% ATM <0.1% CDH1 <0.1% CSF1R<0.1% CTNNB1 <0.1% ERBB4 <0.1% EZH2 <0.1% FGFR1 <0.1% FGFR2 <0.1% FGFR3<0.1% FLT3 <0.1% GNA11 <0.1% GNAQ <0.1% GNAS <0.1% HNF1A <0.1% HRAS<0.1% IDH1 <0.1% IDH2 <0.1% JAK2 <0.1% JAK3 <0.1% KDR <0.1% KIT <0.1%MLH1 <0.1% MPL <0.1% NPM1 <0.1% PDGFRA <0.1% PTPN11 <0.1% RET <0.1%SMAD4 <0.1% SMARCB1 <0.1% SMO <0.1% SRC <0.1% STK11 <0.1% TERT <0.1% VHL<0.1% LOD: Limit of Detection. The minimum detectable mutant allelefrequency for this specimen in which 80% of somatic variants isdetected.

TABLE 3 Allele frequency of altered circulating cell-free DNA detectedin this patient cfDNA with cfDNA without Gene alterations (%)alterations (%) BRAF V600E 8.9 91.1 NRAS Q61K 6.2 93.8 JAK V617F 1.598.6

TABLE 4 Genomic alterations detected in selected genes Detected: 51Genomic Alterations Gene Chromosome Position Mutation (nt) Mutation (AA)Percentage Cosmic ID DBSNP ID KRAS 12 25368462 C > T 100.0% rs4362222ALK 2 29416572 T > C I1461V 100.0% rs1670283 ALK 2 29444095 C > T 100.0%rs1569156 ALK 2 29543663 T > C Q500Q 100.0% rs2293564 ALK 2 29940529 A >T P234P 100.0% rs2246745 APC 5 112176756 T > A V1822D 100.0% rs459552CDKN2A 9 21968199 C > G 100.0% COSM14251 rs11515 FGFR3 4 1807894 G > AT651T 100.0% rs7688609 NOTCH1 9 139410424 A > G 100.0% rs3125006 PDGFRA4 55141055 A > G P567P 100.0% rs1873778 HRAS 11 534242 A > G H27H 100.0%COSM249860 rs12628 EGFR 7 5214348 C > T N158N 99.9% COSM42978 rs2072454TP53 17 7579472 G > C P72R 99.8% rs1042522 APC 5 112162854 T > C Y486Y55.0% rs2229992 APC 5 112177171 G > A P1960P 53.8% rs465899 EGFR 755266417 T > C T903T 53.6% rs1140475 APC 5 112176325 G > A G1678G 53.2%rs42427 APC 5 112176559 T > G S1756S 53.0% rs866006 EGFR 7 55229255 G >A R521K 53.0% MET 7 116397572 A > G Q648Q 52.7% APC 5 112175770 G > AT1493T 52.7% rs41115 EGFR 7 55249063 G > A Q787Q 52.6% rs1050171 NOTCH19 139411714 T > C 52.4% rs11145767 EGFR 7 55238874 T > A T629T 52.0%rs2227984 ERBB2 17 37879588 A > G I655V 51.6% rs1136201 NOTCH1 9139397707 G > A D1698D 51.3% COSM33747 rs10521 ALK 2 30143499 G > C L9L51.0% rs4358080 APC 5 112164561 G > A A545A 51.0% rs351771 FLT3 1328610183 A > G 50.8% rs2491231 NOTCH1 9 139418260 A > G N104N 50.5%rs4489420 ALK 2 29444076 G > T 50.4% rs1534545 PIK3CA 3 178917005 A > G50.3% rs3729674 NOTCH1 9 139412197 G > A 50.2% rs9411208 ALK 2 29455267A > G G845G 50.0% COSM148825 rs2256740 KIT 4 55593464 A > C M541L 49.9%COSM28026 NOTCH1 9 139391636 G > A D2185D 48.9% rs2229974 PDGFRA 455152040 C > T V824V 48.9% COSM22413 rs2228230 ALK 2 29416481 T > CK1491R 48.9% COSM1130802 rs1881420 ALK 2 29445458 G > T G1125G 48.6%rs3795850 NOTCH1 9 139410177 T > C 48.5% rs3124603 RET 10 43613843 G > TL769L 48.2% rs1800861 EGFR 7 55214443 G > A 48.0% rs7801956 ALK 229416366 G > C D1529E 47.2% rs1881421 EGFR 7 55238087 C > T 45.5%rs10258429 RET 10 43615633 C > G S904S 44.8% rs1800863 BRAF 7 140453136A > T V600E 8.9% COSM476 NRAS 1 115256530 G > T Q61K 6.2% COSM580rs121913254 JAK2 9 5073770 G > T V617F 1.5% COSM12600 rs77375493

Example 4. Determining Patient-Specific Limits of Detection for GenesAnalyzed by Guardant360™ Assays

Using the method of Example 3, Genetic alterations in cell-free DNA of apatient are detected. The sequence reads of these genes include exonand/or intron sequences.

Limits of detection of the test are shown in Table 5. The limits ofdetection values are dependent on cell-free DNA concentration andsequencing coverage for each gene.

TABLE 5 Limits of Detection of selected genes in a patient usingGuardant Complete Exon and Partial Intron Coverage APC 0.1% AR * 0.2%ARID1A BRAF * 0.1% BRCA1 BRCA2 CCND1 * CCND2 * CCNE1 * CDK4 * CDK6 *CDKN2A 0.1% CDKN2B EGFR * <0.1% ERBB 2 * 0.1% FGFR1 * <0.1% FGFR2 * 0.1%HRAS 0.1% KIT* 0.1% KRAS * 0.1% MET* 0.1% MYC * 0.1% NF1 NRAS 0.1%PDGFRA * 0.1% PIK3CA* 0.1% PTEN 0.1% RAF1 * TP53 0.1% Exons Covered withReported Somatic Mutations AKT1 0.1% ALK <0.1% ARAF ATM 0.1% CDH1 0.1%CTNNB1 0.1% ESR1 EZH2 0.1% FBXW7 0.1% FGFR3 0.1% GATA3 GNA11 0.1% GNAQ0.1% GNAS 0.1% HNF1A 0.1% IDH1 0.1% IDH2 0.1% JAK2 0.1% JAK3 0.1% MAP2K1MAP2K2 MLH1 0.1% MPL 0.2% NFE2L2 NOTCH1 0.1% NPM1 0.1% PTPN11 0.1% RET0.1% RHEB RHOA RIT1 ROS1 SMAD4 0.1% SMO 0.1% SRC <0.1% STK11 0.2% TERT0.1% VHL 0.2% Fusions ALK <0.1% RET 0.1% ROS1 NTRK1 LOD: Limit ofDetection. The minimum detectable mutant allele frequency for thisspecimen in which 80% of somatic variants is detected. * indicates CNVgenes.

Example 5. Correcting Sequence Errors Comparing Watson and CrickSequences

Double-stranded cell-free DNA is isolated from the plasma of a patient.The cell-free DNA fragments are tagged using 16 differentbubble-containing adaptors, each of which comprises a distinctivebarcode. The bubble-containing adaptors are attached to both ends ofeach cell-free DNA fragment by ligation. After ligation, each of thecell-free DNA fragment can be distinctly identified by the sequence ofthe distinct barcodes and two 20 bp endogenous sequences at each end ofthe cell-free DNA fragment.

The tagged cell-free DNA fragments are amplified by PCR. The amplifiedfragments are enriched using beads comprising oligonucleotide probesthat specifically bind to a group of cancer-associated genes. Therefore,cell-free DNA fragments from the group of cancer-associated genes areselectively enriched.

Sequencing adaptors, each of which comprises a sequencing primer bindingsite, a sample barcode, and a cell-flow sequence, are attached to theenriched DNA molecules. The resulting molecules are amplified by PCR.

Both strands of the amplified fragments are sequenced. Because eachbubble-containing adaptor comprises a non-complementary portion (e.g.,the bubble), the sequence of the one strand of the bubble-containingadaptor is different from the sequence of the other strand (complement).Therefore, the sequence reads of amplicons derived from the Watsonstrand of an original cell-free DNA can be distinguished from ampliconsfrom the Crick strand of the original cell-free DNA by the attachedbubble-containing adaptor sequences.

The sequence reads from a strand of an original cell-free DNA fragmentare compared to the sequence reads from the other strand of the originalcell-free DNA fragment. If a variant occurs in only the sequence readsfrom one strand, but not other strand, of the original cell-free DNAfragment, this variant will be identified as an error (e.g., resultedfrom PCR and/or amplification), rather than a true genetic variant.

The sequence reads are grouped into families. Errors in the sequencereads are corrected. The consensus sequence of each family is generatedby collapsing.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. It is not intendedthat the invention be limited by the specific examples provided withinthe specification. While the invention has been described with referenceto the aforementioned specification, the descriptions and illustrationsof the embodiments herein are not meant to be construed in a limitingsense. Numerous variations, changes, and substitutions will now occur tothose skilled in the art without departing from the invention.Furthermore, it shall be understood that all aspects of the inventionare not limited to the specific depictions, configurations or relativeproportions set forth herein which depend upon a variety of conditionsand variables. It should be understood that various alternatives to theembodiments of the invention described herein may be employed inpracticing the invention. It is therefore contemplated that theinvention shall also cover any such alternatives, modifications,variations or equivalents. It is intended that the following claimsdefine the scope of the invention and that methods and structures withinthe scope of these claims and their equivalents be covered thereby.

What is claimed is:
 1. A system for estimating a number ofdouble-stranded deoxyribonucleic acid (DNA) molecules in a sample,comprising: (a) a communication interface that receives, over acommunication network, sequence reads generated by a nucleic acidsequencer, wherein the sequence reads are derived from a plurality ofthe double-stranded DNA molecules from the sample or progenypolynucleotides thereof; and (b) a computer in communication with thecommunication interface, wherein the computer comprises one or morecomputer processors and a computer readable medium comprisingmachine-executable code that, upon execution by the one or more computerprocessors, implements a method comprising: (i) receiving, over thecommunication network, a set of sequence reads comprising at least1,000,000 sequence reads generated by the nucleic acid sequencer; (ii)determining, from among the set of sequence reads, a first quantitativemeasure of individual double-stranded DNA molecules for which bothstrands are detected; (iii) determining, from among the set of sequencereads, a second quantitative measure of individual double-stranded DNAmolecules for which only one strand is detected; (iv) inferring, fromthe first quantitative measure and the second quantitative measure, athird quantitative measure of individual DNA molecules for which neitherstrand is detected; and (v) estimating a number of double-stranded DNAmolecules in the sample based on the first quantitative measure, thesecond quantitative measure, and third quantitative measure.
 2. Thesystem of claim 1, wherein the method implemented by themachine-executable code further comprises, prior to (b)(ii), groupingsequence reads from the set of sequence reads into families, wherein afamily is representative of an individual double-stranded DNA moleculefrom the sample.
 3. The method of claim 2, wherein the grouping is basedat least on sequence information from nucleic acid sequences of thedouble-stranded DNA molecules.
 4. The system of claim 1, wherein themethod implemented by the machine-executable code further comprises,prior to (b)(ii), mapping the sequence reads to a reference sequence. 5.The system of claim 4, wherein the implemented method further comprises,prior to (b)(ii), grouping sequence reads from the set of sequence readsinto families based at least on sequence information from a start pointof a given sequence read from among the sequence reads at which thegiven sequence read is determined to start mapping to the referencesequence and/or a stop point of the given sequence read at which thegiven sequence read is determined to stop mapping to the referencesequence.
 6. The system of claim 2, wherein the sequence reads comprisenucleic acid sequences of molecular barcodes, and the grouping is basedat least on sequence information from the molecular barcodes.
 7. Thesystem of claim 6, wherein the method implemented by themachine-executable code further comprises distinguishing sequence readsderived from a first strand of a given double-stranded DNA molecule fromsequence reads derived from a second strand of the double-stranded DNAmolecule based at least on the molecular barcodes.
 8. The system ofclaim 6, wherein the grouping is based on a combination of sequenceinformation from the molecular barcodes and sequence information from astart point of a given sequence read from among the sequence reads atwhich the given sequence read is determined to start mapping to thereference sequence and/or a stop point of the given sequence read atwhich the given sequence read is determined to stop mapping to thereference sequence.
 9. The system of claim 8, wherein the methodimplemented by the machine-executable code further comprises, for aplurality of the families, collapsing sequence reads within a givenfamily of the plurality of the families into a consensus sequence. 10.The system of claim 9, wherein the first quantitative measure and secondquantitative measure are the number of consensus sequences.
 11. Thesystem of claim 2, wherein the first quantitative measure and secondquantitative measure are the number of families.
 12. The system of claim1, wherein the first quantitative measure and the second quantitativemeasure are probability distributions.
 13. The system of claim 1,wherein the method implemented by the machine-executable code furthercomprises assessing a quality of the sequence reads.
 14. The system ofclaim 1, wherein the method implemented by the machine-executable codefurther comprises determining copy number variation (CNV) in the sampleby determining a normalized quantitative measure based on the estimatednumber of double-stranded DNA molecules determined in (b)(v) at each ofone or more genetic loci of a reference sequence.
 15. The system ofclaim 1, wherein the computer readable medium comprises a memory, a harddrive, or a computer server.
 16. The system of claim 1, wherein thecommunication network comprises a telecommunication network, a datanetwork, the Internet, an extranet, or an intranet.
 17. The system ofclaim 1, wherein the communication network comprises one or morecomputer servers capable of performing distributed computing.
 18. Thesystem of claim 17, wherein distributed computing is cloud computing.19. The system of claim 1, wherein the communication network comprises astorage device comprising the genetic sequence reads stored thereon. 20.The system of claim 1, wherein the computer is located on a computerserver that is at a location remote from the nucleic acid sequencer. 21.The system of claim 1, further comprising an electronic display incommunication with the computer over a network, wherein the electronicdisplay comprises a user interface for displaying results uponimplementing (b)(i)-(b)(v).
 22. The system of claim 21, wherein the userinterface is a graphical user interface (GUI) or web-based userinterface.
 23. The system of claim 21, wherein the electronic display isin a personal computer of an internet-enabled computer.
 24. The systemof claim 23, wherein the internet-enabled computer is located at alocation remote from the computer.
 25. The system of claim 1, whereinthe one or more computer processors are part of an integrated circuit.26. A system for determining a number of double-strandeddeoxyribonucleic acid (DNA) molecules in a sample that map to a locus ofa reference sequence, comprising: (a) a communication interface thatreceives, over a communication network, sequence reads generated by anucleic acid sequencer, wherein the sequence reads are derived from aplurality of the double-stranded DNA molecules from the sample orprogeny polynucleotides thereof; and (b) a computer in communicationwith the communication interface, wherein the computer comprises one ormore computer processors and a computer readable medium comprisingmachine-executable code that, upon execution by the one or more computerprocessors, implements a method comprising: (i) receiving, over thecommunication network, a set of sequence reads comprising at least1,000,000 sequence reads generated by the nucleic acid sequencer; (ii)grouping the set of sequence reads into families, wherein a family isrepresentative of an individual double-stranded DNA molecule from thesample; (iii) determining, from among the families, a first quantitativemeasure of individual double-stranded DNA molecules that map to thelocus for which both strands are detected; (iv) determining, from amongthe families, a second quantitative measure of individualdouble-stranded DNA molecules that map to the locus for which only onestrand is detected; and (v) determining a number of double-stranded DNAmolecules from the sample that map to the locus based at least on thefirst quantitative measure and the second quantitative measure.
 27. Thesystem of claim 26, wherein the grouping in (b)(ii) is based on: (1)sequence information from nucleic acid sequences of the double-strandedDNA molecules; or (2) sequence information from a start point of a givensequence read from among the sequence reads at which the given sequenceread is determined to start mapping to the reference sequence and/or astop point of the given sequence read at which the given sequence readis determined to stop mapping to the reference sequence; or
 28. Thesystem of claim 26, wherein the set of sequence reads comprise nucleicacid sequences of molecular barcodes, and the grouping in (b)(ii) isbased on: (1) sequence information from the molecular barcodes; or (2) acombination of sequence information from the molecular barcodes andsequence information from a start point of a given sequence read fromamong the sequence reads at which the given sequence read is determinedto start mapping to the reference sequence and/or a stop point of thegiven sequence read at which the given sequence read is determined tostop mapping to the reference sequence.
 29. The system of claim 28,wherein the method implemented by the machine-executable code furthercomprises distinguishing sequence reads derived from a first strand of adouble-stranded DNA molecule from sequence reads derived from a secondstrand of the double-stranded DNA molecule based at least on sequenceinformation from the molecular barcodes.
 30. The system of claim 26,wherein the determining in (b)(v) is further based on a thirdquantitative measure of individual double-stranded DNA molecules thatmap to the locus for which neither strand is detected that is determinedusing the first quantitative measure and the second quantitativemeasure.